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Archivists are charged with the preservation of their collections by reducing deterioration because of temperature, relative humidity, atmospheric pollutants and other factors. The methods archivists use to preserve their collections may have a negative impact on the environment. This paper aims to identify factors for building environmentally sustainable archives to help guide archival environmental sustainability practices.

This paper identifies factors through a literature review, and conducts a content analysis of the websites of seven national/state archives. The analysis focuses on the policy statements of these archives.

The authors found that the literature lists 31 factors under 7 categories: electricity, facilities, water, exhibitions, pollution, collection practices and education and outreach. The content analysis of the policy documents or statements demonstrated that archives applied and addressed mostly “resource-related” efforts to protect the environment, such as factors related to electricity, facilities, water and pollution. However, factors related to “work-related” efforts, such as exhibitions, collection practices and education and outreach, were ignored.

This study can provide insights to archivists on current implementation and help to guide their further environmental sustainability practices.

Little is known regarding archivists’ implementation of environmentally sustainable practices. This study focuses on identifying factors for environmental sustainability of archives addressed by literature and existing archives, trying to find the gap between literature and practice.

This paper aims to establish a theoretical search model on academic social networking sites (ASNSs).

Based on the characteristics of ASNSs and a previous extended sense-making model, this paper first presented an initial model of searching on ASNSs. Next, an online survey was conducted on ResearchGate to understand the search processes and outcomes with the help of a survey questionnaire. In total, 359 participants from 70 countries participated in this online survey. The survey results provided a basis for modifying the initial model.

Results showed that the theoretical model of searching on ASNSs included motives for searching on ASNSs, identification of needs, search triggered by information needs, search triggered by social needs and outcomes. The search triggered by information needs was significantly positively correlated with learning outcomes. Besides learning outcomes, searching on ASNSs could help user amplify their social networks and promote research dissemination.

Understanding users’ search habits and knowledge acquisition can provide insights for ASNSs to design an interface to support searching and enhance learning. Moreover, the proposed model can help users recognize their knowledge status and learning effects and improve their learning efficiency.

This paper contributes to establishing a theoretical model to understand users’ search process and outcomes on ASNSs.

This paper aims to investigate the influence of over-allocation and under-allocation of family board seats on the corporate investment efficiency.

Based on the perspective of altruistic behavior, this paper theoretically analyzes the relationship between the preference of family board seats allocation and corporate investment efficiency, and designs the research. On this basis, we use STATA14.0 as an analysis tool to empirically test the relationship between the preference of family family board seats allocation and corporate investment efficiency, and consider the impact of different governance scenarios.

This study finds that firms with a higher over-allocation degree of family board seats invest more efficiently, evidenced by significantly suppressed over-investment rather than mitigated under-investment. However, we do not find evidence that the higher degree of under-allocation of family board seats contribute to lower corporate investment efficiency. Additionally, this study finds that the positive relationship between the over-allocation degree of family board seats and corporate investment efficiency is more pronounced for firms with higher separation of cash flow rights and control rights, and weaker regional law system environment. Our mechanism discussion shows that the higher over-allocation level of family board seats contributes to the mitigation of agency costs for family firms by reducing the tendency for non-family boards to vote “against board proposals” and the appropriation behavior of the controlling family, and eventually improving corporate investment efficiency.

This paper examines the relationship between the preference of family board seats allocation and corporate investment efficiency from the perspective of altruistic behavior. Unlike previous studies, this paper distinguishes the governance effects arising from over-allocation and under-allocation of family board seats. Additionally, different governance scenarios are incorporated into the decision-making mechanism of the board of family firms, and the influences of the divergence of cash-flow and control rights and a weaker regional law system on the governance effect of the preference of family board seat allocation are analyzed.

Previous research has little specific guidance on how to improve large infrastructures’ risk analysis. This paper aims to propose a practical risk analysis framework across the project lifecycle with Bayesian Networks (BNs).

The framework includes three phases. In the qualitative phase, primary risks were identified by literature reviews and interviews; questionnaires were used to determine key risks at each project stage and causal relationships between stage-related risks. In the quantitation, brainstorming and questionnaires, and techniques of ranked nodes/paths, risk map and Bayesian truth serum were adopted. Then, a BN-based risk assessment model was developed, and risk analysis was conducted with AgenaRisk software.

Twenty key risks across the lifecycle were determined: some risks were recurring and different risks emerged at various stages with the construction and feasibility most risky. Results showed that previous stages’ risks significantly amplified subsequent stages’ risks. Based on the causality of stage-related risks, a qualitative model was easily constructed. Ranked nodes/paths facilitated the quantification by requiring less statistical knowledge and fewer parameters than traditional BNs. As articulated by a case, this model yielded very simple and easy-to-understand representations of risks and risk propagation pathways.

Rare research has developed a BN risk assessment model from the perspective of project stages. A structured model, a propagation network among individual risks, stage-related risks, and the final adverse consequence, has been designed. This research provides practitioners with a realistic risk assessment approach and further understanding of dynamic and stage-related risks throughout large infrastructures’ lifecycle. The framework can be modified and used in other real-world risk analysis where risks are complex and develop in stages.

The study aims to investigate the impact of industrial robot application on corporate labor cost stickiness and labor investment efficiency in China.

Using the textual analysis to construct firm-level industrial robot application indicators in China, we implement the methodology in Anderson et al. (2003) and Banker and Byzalov (2014) to estimate cost stickiness.

We argue that the industrial robot uses in China would increase firms’ labor adjustment costs by increasing the employment scale and upgrading the employment structure (i.e. by employing more high-skilled and high-educated labor). Consistent with our expectation through the channel of labor adjustment costs, the use of robotics increases firms’ labor cost stickiness. We further find that the positive impact is more significant among labor-intensive industries, and among state-owned enterprises with lower labor adjustment flexibility. We also find that industrial robot uses do not decrease the labor cost stickiness even when robots are more likely to substitute labor. Finally, we find that industrial robot uses significantly facilitate more efficient hiring practices by mitigating overinvestment in labor (i.e. over-hiring).

Against the backdrop of intelligent manufacturing worldwide, our study sheds new insight into the effects of new technologies on corporate labor cost behavior in developing countries. We contribute to scant studies examining how robotics, AI adoption or other automation technologies (e.g. specialized machinery, software, etc.) affect corporate cost behavior.