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The Public–Private Partnership (PPP) modality plays an important role in the procurement of global infrastructure projects. Regarding PPP's complex transaction structure, pricing of a PPP project is critical to both parties where the government pursues a high value for money (VFM) and the investor strives to maximize its financial gains. Despite the straightforward win–win principle, a formidable compromise is often the case to end up with a fairly acceptable price, subject to many determinants such as the risk profile, expected return, technological innovation and capacities of both parties. Among them, this study chooses to examine the “managing flexibility” (MF) capacity of investors in pricing of a PPP project, in light of the widely recognized importance of a real-option perspective toward the long term, complex and uncertain PPP arrangement. This study addresses two major questions: (1) how is MF in PPP projects to be valued and (2) how are PPP projects to be priced when considering a project's MF value.

A binomial tree model is used to evaluate the MF value in PPP projects. Based on the developed MF pricing model, net present value (NPV) and adjusted VFM value are then calculated. Finally, a multi-objective decision-making method (MODM) was adopted to determine the optimal level of returns based on invested capital (ROIC), return on operation maintenance (ROOM) and concession period.

The applicability and functionality of the proposed model is investigated using a real project case. For a given return, extended NPV and adjusted VFM value were calculated and analyzed using sensitivity analysis. Factor influence is shown by the model to be dependent on factor impact on cash flow. Subsequently, a multi-objective decision-making (MODM) model was adopted to determine the optimal level of returns, where the solution approximates the real-world bidding price. Results confirm that the pricing model provides a reliable and practical PPP proposal pricing tool.

This study proposes an integrated framework for valuing MF in PPP projects and thus more accurately determine optimal pricing of PPP projects than revealed in extant research. The model offers a practical tool to aid in the valuation of PPP projects.

The objective of this research is to methodically categorize the various types of Open Government Data (OGD) stakeholders, and to elucidate the intricate network relationships among OGD stakeholders, along with the underlying mechanisms that shape their formation.

To comprehend the collaboration mechanism of stakeholders in the OGD ecosystem, the authors constructed an OGD multi-stakeholder relationship network by using data from the Shandong Province Data Application Innovation and Entrepreneurship Competition. Based on the structural social capital theory and exponential random graph model (ERGM), an analytical framework was established to explore the formation mechanism of the collaborative network of OGD multi-stakeholder.

The results indicate that multi-stakeholder collaboration among government, enterprises and the public is crucial for achieving OGD goals. Organizing OGD competitions serves as an effective mechanism for solidifying and maintaining relationships among OGD stakeholder groups. Degree centrality and structural parameters reveal a Matthew effect within the connection process of the OGD ecosystem's collaborative network. Additionally, there is evidence of agglomeration and transferability within the network's structure.

This study contributes to the understanding regarding the formation mechanism of OGD stakeholders. The findings have implications for developing multi-stakeholder relationship networks of OGD and driving OGD initiatives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0284

The rapid development of e-commerce live streaming is inseparable from the important role of live-streamer. However, research exploring the impacts of live-streamers’ characteristics on customer engagement in different types of live streaming rooms remains limited. This study aims to examine the impacts of live-streamers’ characteristics (i.e. expertise, moral reputation, popularity, responsiveness) on consumer engagement in e-commerce live streaming, and verify whether there are differences in the impact of live-streamers’ characteristics on consumer experience for different types of live-streamers.

Data were gathered from 266 Chinese adults who have experience watching e-commerce live streaming. Partial least squares techniques were used to verify the model.

The results show that expertise, moral reputation, popularity, and responsiveness of live-streamers have positive impacts on consumer engagement through perceived trust and perceived pleasure. Moreover, through multi-group analysis, this study found that compared with enterprise live-streamers, the moral reputation of individual live-streamers exerts a stronger effect on consumers’ perceived trust, and the popularity of individual live-streamers exerts a stronger effect on consumers’ perceived pleasure. Compared with individual live-streamers, the responsiveness of enterprise live-streamers exerts a stronger effect on consumers’ perceived pleasure.

This study contributes to e-commerce live streaming research by categorizing live-streamers into two types: individual live-streamers and enterprise live-streamers, and verifying the specific difference in the impacts of live-streamers’ characteristics on consumer experience for the two types of live-streamers.

Travel bragging, driven by impression management, is common on social media. However, straightforward bragging can create negative perceptions. To mitigate this, tourists often turn to humblebragging, but its effectiveness is unclear. This study aims to examine whether humblebragging elicits more positive responses from viewers than straightforward bragging.

Drawing on social comparison theory and compensation theory, this paper developed a moderated mediation model to explore the impact of bragging type (bragging vs humblebragging) on viewer behavior. The model was validated through two scenario-based experiments.

The results reveal the double-sword effect of humblebragging: Humblebragging elicits stronger benign and malicious envy than bragging. Benign envy mediates the relationship between bragging type and consumption intention, while malicious envy mediates between bragging type and avoidance/gossip. Perceived deservingness moderates the effect of bragging type on envy and the mediation processes. When viewers perceive the poster’s advantage as deserving, humblebragging elicits more benign envy than bragging. When perceived as undeserving, humblebragging leads to more malicious envy.

This study is innovative in validating the double-edged sword effect of humblebragging and identifying perceived deservingness as a boundary condition.

This study aims to explore how artificial intelligence (AI) can enhance mental health care in Tanzania, focusing on its potential to enhance mental health services and address challenges in a low-resource setting.

A qualitative case study approach was used, with data collected through semi-structured interviews and focus group discussions involving key stakeholders in mental health and AI, including policymakers, technical experts, health-care providers and patient advocacy groups. Thematic analysis was used to identify key themes related to the opportunities and barriers to AI integration in mental health care.

This study identified several benefits of AI in mental health care, including improved diagnostic accuracy, personalised treatment and the potential for real-time monitoring of patients. However, significant barriers to AI adoption remain, such as infrastructure limitations, data privacy concerns and the need for training and resources to effectively integrate AI into mental health services.

This study contributes to the growing literature on AI in health care by focusing on its application in mental health care in Tanzania, a low-resource setting. The research provides valuable insights into how AI can bridge gaps in mental health service delivery, particularly in underserved regions, while highlighting the challenges that must be addressed for successful implementation.

This study aims to investigate the relationship between stock markets, environmental, social and governance (ESG) factors and Shariah-compliant in an integrated framework.

The authors employ the multivariate factor stochastic volatility (mvFSV) framework to extract the volatility of the different sectoral indices. Based on this evidence, the authors employ the quantile vector autoregressive (QVAR) approach to examine the dynamic spillover connectedness among the aforementioned indices.

The study emphasizes the following major findings: (1) significant time-varying spillover connectedness across quantiles, (2) bidirectional and asymmetric spillover effect among the ESG index and the other sectoral indices, (3) the strength of spillover connectedness is time-varying across quantiles, (4) based on the perspective of portfolio optimization, ESG market is a significant strong forecasting contributor to conventional and Shariah-compliant markets, (5) overall, the findings point out serious quantile pass-through effect among ESG index and the other sectoral indices during the COVID-19 health crisis.

This study extends the previous literature in the following ways. First, to the best of the researchers’ knowledge, none of the existing studies have investigated the relationship between stock markets, ESG factors and Shariah-compliant in an integrated framework. Second, this study extends the previous scholarships by applying the mvFSV. Third, the authors propose a new rolling version to estimate dynamic spillovers, namely the rolling-window quantile VAR method. This approach provides a great advantage in computing the dynamics of return and variance spillover between variables in terms not only of the overall factor but also of the net (pairwise) aspect.

The purpose of this systematic literature review (SLR) is to examine the theoretical landscape of knowledge hiding (KH) research, identifies prevailing theories, the different ways KH is understood within these theories and the underlying assumptions that shape these views. Based on this, ideas for further research are derived to advance the theoretical basis of KH studies.

Using a theory-based SLR, the authors analysed 170 scientific papers from Scopus and Web of Science. This involved thematic analysis to categorise theories frequently applied in KH research and a detailed examination to link core assumptions to these theoretical perspectives.

The analysis revealed a reliance on 86 distinct theories, with a notable emphasis on social exchange theory and conservation of resources theory. KH is predominantly conceptualised as a negative, objective, reactive and relational behaviour rooted in social reciprocity and resource conservation. The review uncovers the multifaceted nature of KH, challenging the field to incorporate broader theoretical views that encompass positive aspects, subjective experiences, strategic intentions and non-relational determinants of KH.

To the best of the authors’ knowledge, this is the first study to systematically map and analyse the theoretical underpinnings of KH research. It offers a unique contribution by categorising the diverse theories applied in KH studies and explicitly linking these theories to their inherent assumptions about KH. This approach provides a comprehensive overview that not only identifies gaps in the current research landscape but also proposes alternative theoretical perspectives for exploring KH, thereby setting a new direction for future studies in this field.

This systematic review aims to comprehensively explore the existing literature on the use of corporate communication within the realm of social media.

A total of 136 peer-reviewed journal articles are explored and analysed using both performance and bibliometric analysis.

This review identifies five main findings: (1) trends in corporate social media research that highlight the growth trajectory of research on social media use for corporate disclosure, (2) geographical coverage of studies indicating the concentration of research in certain regions, such as the USA, followed by China and the UK, with notable gaps in others, such as developing countries, (3) theoretical frameworks employed demonstrate that various theoretical frameworks are utilized, although a significant portion of the studies do not specify any theoretical underpinning, (4) social media platforms studied, confirming Twitter to be the most studied channel followed by Facebook and (5) thematic analysis of articles on disclosure type that categorized the articles using bibliometric analysis into five themes of disclosure: general disclosure, corporate social responsibility-related information, financial information, CEO announcements and strategic news communication. A subsequent cross-theme analysis classifies disclosure determinants and consequences of corporate social media usage.

Through a comprehensive and systematic analysis of existing research, this review offers novel insights into the current state of corporate communication on social media. It consolidates current knowledge, highlights under-explored areas in the existing literature and proposes new directions and potential avenues for future research.

Personal thermal management by controlling the radiation energies of both the body and the sun can be used in all environments and contributes to sustainability components with the advantages of energy saving, low chemical usage and comfort enhancements under dynamic conditions. In this study, passive radiative heating nanocomposite films were produced using sodium alginate as the matrix and zinc oxide (ZnO) and aluminum oxide (Al₂O₃) nanoparticles as nanofillers having far infrared radiation reflecting, hence passive heating functions.

Nanocomposite film solutions were prepared by stirring sodium alginate powder, deionized water, ZnO and Al₂O₃ nanoparticles (20% wt of matrix polymer) with surfactant using magnetic and ultrasonic stirrers in turn. Films produced within Petri dishes after drying at room temperature were analyzed by FT-IR, UV-VIS-NIR spectroscopy and SEM for chemical, radiation management and morphological characteristics, respectively. Emissivity values giving idea about the heating performances of the films were determined with an IR camera and a hotplate system. Moreover, direct heating performances were measured by the hotplate system including a far-infrared lamp.

Results showed that the emissivity of the films increased by approximately 18% and 16% with ZnO and Al₂O₃ nanoparticles, respectively. Moreover, NaAlg–Al₂O₃ nanocomposite film exhibited passive radiative heating performance of 3.58 °C, higher than the heating performance of NaAlg–ZnO nanocomposite film which is 2.97 °C when compared to the reference NaAlg film. These results indicate that both NaAlg–ZnO and especially NaAlg–Al₂O₃ nanocomposite films have excellent far-infrared emission and absorption properties ensuring a significant heating effect.

In addition to other clothing types, the heating performance obtained with the produced nanocomposite structures may be applied to different types of cosmetic/medical applications (beauty mask, wound dresses, etc.) enabling skincare/healing with the advantage of the sodium alginate matrix.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.