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Within the umbrella of technology and vocational education (TVET), technology or technical education in higher institutions of learning is obligated to produce the required manpower needed in the industry. Thus, it is pertinent to explore the interaction between the industry and higher education students. Drawing on the tenets of theory of planned behavior (TPB), this study offers valuable insights into the nomological networks of work-integrated learning (WIL), perceived behavioral control (PBC), subjective norm (SBN), personal attitude (PAT) and job search intention (JSI).

The study applied a structurally hypothesized model that was drawn from the TPB to collect data for the constructs. Using a cross-sectional survey after the WIL experiences of the students, we collected data from technology education undergraduates (N = 214) in their final academic year from universities in Nigeria.

With structural equation modeling, the study found that WIL is directly associated with JSI, PBC, SBN and PAT. In line with the tenets of the TPB, simple mediation models were supported about the influence of WIL on JSI via PBC and PAT discretely but not via SBN. Further, the results support two paths of serial mediation models, indicating sequential indirect links between WIL and JSI via SBN and PBC, as well as via SBN and PAT.

Our findings have implications for higher education practitioners, industry experts and employers of labor.

Although extant literature has relatively shown that WIL impacts employability skills, this study has remarkably shown the WIL-JSI nexuses within the variables of TPB.

This study aims to examine the influence of brand awareness on brand equity with the mediating role of brand association and perceived quality.

This research is applied in terms of the objective and descriptive survey in terms of methodology because its objective is to describe the studied conditions or phenomena and also the relationship between research variables that have been conducted in the field method.

The results explained that 53% of respondents are female and 47% of respondents are male. Most respondents are between 30 and 40 years old, and the youngest respondents are less than 30 years old. Most respondents were specialists, and the 134 respondents were subspecialists. The results explained that the significance level of the Kolmogorov–Smirnov test for research variables is less than 0.05 and has an abnormal distribution. The abnormality of the data in this study directed the authors to use PLS software to analyze the data.

To the best of the authors’ knowledge, it is the first time that research is prepared to study the impact of these marketing parameters on dentists’ prescription behavior.

Sharing project information is critical for the success of medical crowdfunding campaigns. However, few users share medical crowdfunding projects on their social networks, and the sharing behavior of medical crowdfunding projects on social networking sites has not been well studied. Therefore, this study explored the factors and potential mechanisms influencing users’ sharing behaviors on networking sites.

A research model was developed based on the attribution-affect model of helping and social capital theory. Data were collected using a longitudinal survey. Partial least squares structural equation modeling was used to analyze the collected data. We conducted post hoc analyses to validate the results of the quantitative analysis.

The analysis results verified the effects of perceived external attribution, perceived uncontrollable attributions, and perceived unstable attributions on sympathy and identified the effect of sympathy and social characteristics of medical crowdfunding users on sharing behavior.

This research provides a comprehensive theoretical understanding of users’ sharing behavior characteristics and provides implications for enhancing the efficiency of medical crowdfunding activities.

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.

Social media (SM) platforms tempt individuals to communicate their perspectives in real-time, rousing engaging discussions on countless topics. People, besides using these platforms to put up their problems and solutions, also share activist content (AC). This study aims to understand why people participate in activist AC sharing on SM by investigating factors related to planned and unplanned human behaviour.

The study adopted a quantitative approach and administered a close-ended structured questionnaire to gather data from 431 respondents who shared AC on Facebook. The data was analysed using hierarchical regression in SPSS.

The study found a significant influence of both planned (perceived social gains (PSGs) , altruism and perceived knowledge (PK)) and unplanned (extraversion and impulsiveness) human behaviour on activist content-sharing behaviour on SM. The moderating effect of enculturation and general public opinion (GPO) was also examined.

Sharing AC on SM is not like sharing other forms of content such as holiday recommendations – the former can provoke consequences (sometimes undesirable) in some regions. Such content can easily leverage the firehose of deception, maximising the vulnerability of those involved. This work, by relating human behaviour to AC sharing on SM, offers significant insights to enable individuals to manage their shared content and waning probable consequences.

This work combined two opposite constructs of human behaviour: planned and unplanned to explain individual behaviour in a specific context of AC sharing on SM.

The proliferation of health misinformation on social media has increasingly engaged scholarly interest. This research examines the determinants influencing users’ proactive correction of health misinformation, a crucial strategy in combatting health misbeliefs. Grounded in the elaboration likelihood model (ELM), this research investigates how factors including issue involvement, information literacy and active social media use impact health misinformation recognition and intention to correct it.

A total of 413 social media users finished a national online questionnaire. SPSS 26.0, AMOS 21.0 and PROCESS Macro 4.1 were used to address the research hypotheses and questions.

Results indicated that issue involvement and information literacy both contribute to health misinformation correction intention (HMCI), while misinformation recognition acts as a mediator between information literacy and HMCI. Moreover, active social media use moderated the influence of information literacy on HMCI.

This study not only extends the ELM into the research domain of correcting health misinformation on social media but also enriches the perspective of individual fact-checking intention research by incorporating dimensions of users’ motivation, capability and behavioral patterns.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2023-0505