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This study aims to investigate how social capital (e.g. cognitive and relational) influences students’ trust (e.g. cognitive and affective) as mediator variables, affecting students’ information sharing activity on Facebook.

The sample consists of 398 valid participants obtained through an online survey and using structural equation modeling (SEM) to test the research hypotheses.

The empirical results indicate that social capital has significant and positive effects on students’ trust (e.g. cognitive and affective-based trust), also mediator variables. Furthermore, the mediator variables partially mediate social capital and information sharing based on the concept of cognition-affection-behavior (CAB).

This study was limited to Indonesian students. Therefore, future study is needed to analyze across cultures and regions. It can help practitioners, regulators and researchers to observe the dynamic behavior on the impact of social capital on social media users’ activities.

Education stakeholders (e.g. lecturers and teachers) can identify the students’ goal and rational concerns to improve their social capital and trust to share information. The government as a regulator needs to support students’ activities on social media to provide updated information regarding economic and social conditions during and after the COVID-19 pandemic.

This study contributes to the literature on virtual communities. Specifically, it considers how social capital influences trust, which subsequently affects information sharing based on the CAB context among Indonesian student’ Facebook users.

This study aimed to explore how the attributes of heritage destinations become constraints on tourists' intention to revisit these destinations and to provide stakeholders with strategies to mitigate travel constraints, thereby increasing tourists' revisit intention.

A quantitative research approach was used, and data collection utilised a convenience sampling method. A total of 1,250 tourists completed self-administered, on-site surveys. To analyse the collected data effectively and to test hypotheses, multilevel analysis models were created.

Heritage-destination attributes are found to mediate the impact of perceived constraints on tourists' intention to revisit heritage destinations. Positive perceptions of heritage and cultural attractions among tourists can mitigate the adverse effects of constraints on their intentions.

This study only focused on examining the downward cross-level impact, i.e. from the macro level to the individual level. This could be beneficial for exploring an upward cross-level impact.

The findings provide strategic guidance for marketing programs to enhance the reputation of heritage destinations. By identifying and establishing attributes specific to heritage destinations that visitors perceive as the most appealing, program designers can effectively target their efforts. Additionally, the findings help stakeholders of heritage destinations develop and provide suitable heritage and cultural attractions and tourism infrastructure in line with tourists' preferences.

This study employed a multilevel approach to examine how heritage-destination attributes and related constraints on travelling on impact individuals' intentions to revisit destinations. The study considered a macro-based perspective to analyse these effects.

The sustainable development goal (SDG 17) is to strengthen the means of implementation and revitalise the global partnership for sustainable development. Partnership is a key strategy for acquiring skills, information and private finance for developing critical infrastructure projects. Existing studies have mostly concentrated on identifying critical success factors (CSFs) for successfully delivering public–private partnerships (PPPs) projects, but the interrelationships among CSFs expected from stakeholders have garnered less attention. The purpose of this study is to examine the interconnections among CSFs of PPP projects while keeping the public in mind, as well as the influencing routes that contribute to success in the PPP implementation process.

The study uses a mixed-method approach that involves both quantitative and qualitative methods of data collection. The data for the study were collected through a well-structured questionnaire from the respondents who are directly or indirectly associated with the PPP project implementation process. The CSF’s framework was built using a literature study and expert interviews; nine hypotheses were developed and evaluated using structural equation modelling. Random and purposive sampling techniques were used for the respondents who were directly or indirectly associated with the PPP project implementation process.

The findings of this study reveal that the relationship between public and private partners is more adherent than collaborative, with unequal responsibilities, powers and resources distributed among the parties involved. The relevance of state rules, the PPP directive and policies in PPP implementation is supported by the positive connection between favourable environments (Fav_Env) and government attributes (Gov_Atr; the path coefficient is 0.405). Moreover, government attributes have a favourable influence on private company attributes (Pvt_Atr; the path coefficient is 0.198), showing that the state plays a substantial role in the private sector’s decision-making process in PPP. The long-term sustainability and success of a PPP project are dependent on the established interrelationships between CSFs.

The established interrelationships between CSFs will contribute to the long-term viability and success of a PPP project. Since PPP is still in its infancy in India, the study of CSFs in PPPs and their interrelationships will help policymakers and other stakeholders better understand the major individual factors impacting the success or failure of PPP initiatives.

Because PPP is still in its infancy in India, the study of CSFs in PPPs and their interrelationships will help policymakers and other stakeholders to better understand the major individual factors impacting the success or failure of PPP initiatives. A key implication of the study borders on providing a better understanding of the relationships between CSFs and public engagement. This is also critical for the long-term viability of PPPs and the enhancement of public service quality.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

This study uses a comprehensive theoretical framework that combines social cognitive theory and neighborhood effect to investigate the influence of neighborhood effects on farmers’ outcome expectations, observational learning and self-efficacy. This study aims is to analyze the mechanisms that underlie the adoption of social media by farmers for knowledge exchange in the agricultural context. Specifically, this research explores the role of neighborhood effects, outcome expectations, observational learning and self-efficacy in shaping farmers’ decision-making process regarding the use of social media platforms for exchanging agricultural knowledge.

The study data was collected through a sample survey conducted among 570 agricultural households residing in the provinces of Thai Nguyen, Cao Bang, Bac Kan and Phu Tho, located in the northern region of Vietnam. To analyze the data, structural equation modeling was used as the statistical technique of choice.

The findings of the study indicate a significant influence of neighborhood effects on outcome expectations, observational learning and self-efficacy. These factors, derived from social cognitive theory, also exhibit a positive association with farmers’ adoption of social media for knowledge exchange. Additionally, the study highlights that neighborhood contribute to a favorable adoption of social media among farmers via outcome expectations, observational learning, and self-efficacy.

The study is limited in examining farmers’ social media adoption for agriculture knowledge exchange in Northern mountainous area of Vietnam. This study could be replicated across various regions or nations, providing comparative insights into the adoption of social media among farmers for knowledge exchange.

The study findings suggest practical and innovative means to promote farmers’ social media adoption for agriculture knowledge exchange.

This study presents a pioneering approach by integrating social cognitive theory and neighborhood effect to elucidate the factors influencing farmers’ adoption of social media for the purpose of agriculture knowledge exchange.