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This paper aims to investigate how and when different dimensions of trust and contracts interact to influence the development to negotiation strategies. Specifically, it explores how different dimensions of trust and contracts are combined to influence dispute negotiation strategies when cooperation parties have or do not have expectations of continuity.

This paper theoretically identified and empirically examined the interaction effect of trust and contract on dispute negotiation strategies in contractor–subcontractor relationships, by developing a conceptual framework and conducting a questionnaire survey comprising more than 300 disputes in the Chinese construction industry. Hierarchical regression analysis was mainly used to test the hypotheses.

This paper finds that contractual control may weaken the effect of goodwill trust in fostering interest-based strategies in the presence of expectations of continuity, while it may strengthen the effect in the absence of expectations of continuity. Contractual coordination negatively moderates the relationship between goodwill trust and interest-based strategies only when parties have little expectations of continuity. Moreover, contractual control enhances the effectiveness of competence trust on fostering interest-based strategies.

This paper provides insights for practitioners to wisely use different governance mechanisms to manage negotiation strategies and generate desired outcomes of dispute resolution.

This paper provides a nuanced understanding of how the two types of governance mechanisms interact, by considering trust and contract as multi-dimensional constructs. It explicit the boundary conditions of both the substitute and complementary relationship between them.

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

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.

Work from home has become as regular as the traditional commuting system after the outbreak of the COVID-19 pandemic. Previous studies have discussed the influence of working at home on the work–family interface. However, there is limited understanding of how diverse workforces manage their work–family issues with various family-friendly policies. This study aims to bridge this research gap by examining the collective influence of work conditions and family-friendly policies on work–family balance.

A survey experiment featuring two working conditions (work from home or commuting) × four family-friendly policies (household subsidy, family-friendly supervisor, financial profit, paid leave vs no policy) was approached based on 703 valid responses in China.

The results indicate that family-friendly policies are more effective under the work-from-home condition than the commuting condition, household subsidies and financial profits are considered more helpful for work–family balance under the work-from-home condition and employees’ policy preferences depend on personal identity and work conditions, which help them maintain work and family issues concurrently.

This study explores the joint impact of work conditions and family-friendly policies from a situational perspective. This study indicated that professional organizations need to perform delicacy management considering policy preferences. Moreover, changing working arrangements help employees facilitate their work–family balance.