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This paper aims to explore the interplay between industrial diversity and sustainable economic development in US counties.

Among other popularly used measures, this study uses an underused measure, Hachman index, to gauge the degree of industrial diversity in the models. To capture the impact of industrial diversity on the local community, this study estimates the relationship of two diversity measures to four traditional socioeconomic indicators: per capita personal income growth, gross domestic product per worker, income inequality ratio and poverty rate.

Statistical results suggest that industrial diversity, which is measured by Hachman index, is significantly related to the four socio-economic indicators. Industrial diversity can positively contribute to regional per capita personal income growth and mitigate income inequality and poverty stress; however, it is negatively related to the gross domestic product (GDP) per worker, which means industrial specialization may contribute to GDP per worker growth.

The findings of this study show that there is a nonlinear relationship between industrial diversity and all socioeconomic indicators. Most of the control variables, human capital variables and business and industry profile variables also display significant and positive impacts on economic development.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

The purpose of this study is to investigate the obstacles to mechatronics adoption in the construction industry from a Nigerian perspective. It aims to fill the knowledge gap by focusing on the specific challenges faced in developing countries, considering the unique contexts and constraints of the Nigerian construction industry.

The study used a comprehensive literature review to identify 26 obstacles to mechatronics adoption. These obstacles were used to develop a well-structured questionnaire, which was then distributed to construction professionals using Google Forms through purposive and snowball sampling techniques. The rankings obtained from the questionnaire responses were analyzed to determine the most significant obstacles.

The study revealed the top five most significant obstacles to mechatronics adoption in the Nigerian construction industry. These obstacles include high costs of operation and maintenance, resistance to adopting new technologies, a lack of standardized protocols, insufficient maintenance capabilities and a lack of government support. Factor analysis revealed five clusters of obstacles: technological-related factors, economic-related factors, capability-related factors, government-related factors and awareness-related factors.

Findings from this study have the potential to inform decision-making, drive policy changes and guide future research efforts aimed at promoting the widespread adoption of mechatronics technologies, ultimately leading to the transformation and improvement of the construction industry as a whole.

This study contributes to the field of mechatronics adoption in the construction industry by addressing the gap in research specific to developing countries such as Nigeria. By identifying and analyzing the obstacles from a Nigerian perspective, the study offers unique insights and original findings.

In the context of carbon peaking and neutrality, effectively controlling agricultural carbon emissions has gained academic attention. As an essential form of agricultural service scale management, this study investigates whether and how trusteeship affects the carbon emission behavior in planting production.

The authors established a theoretical framework to analyze the impact of agricultural production trusteeship on carbon emissions from planting. China's provincial panel data in the 2012–2021 period were selected to test the impact, mechanisms and heterogeneity of agricultural production trusteeship on carbon emissions from planting using the bidirectional fixed effect model and the panel correction standard error regression model.

The findings indicate that agricultural production trusteeship significantly inhibits carbon emissions from planting, especially in the dimensions of fertilizer input, pesticide application, agricultural film use and mechanical fuel. Agricultural production trusteeship primarily affects the intensity of these carbon emissions through contiguous farmland management and planting structure adjustment. Further examinations revealed that the influence of agricultural production trusteeship on carbon emissions from planting was heterogeneous with respect to geographical location, proportion of non-farming income and scale of agricultural production.

This study is the first to systematically evaluate the impact of agricultural production trusteeship on carbon emissions from planting.

This study aims to investigate the impact of pollution control bonds (PCBs) on overinvestment within utility firms.

This empirical study analyzes a data set comprising 215 US energy firms observed from 2011 to 2021, using the ordinary least square regression with standard errors adjusted for firm-level clustering.

The study reveals a negative relationship between PCBs and overinvestment, indicating that PCBs are an effective tool in curbing excessive investment. Additionally, it demonstrates that chief executive officer (CEO) overconfidence diminishes the influence of PCBs on overinvestment. These findings remain robust across various metrics for measuring overinvestment and CEO overconfidence, as well as when alternative estimation methods are used. These results align with insights derived from agency theory and upper echelon theories.

Regulators are encouraged to actively promote the use of PCBs as a financing tool for environmentally focused initiatives. To achieve this, regulatory bodies should enhance their presence within the utility sector, particularly in regions grappling with higher pollution levels. This requires the implementation of strategic policies and regulatory frameworks aimed at mitigating excessive investments. Simultaneously, policymakers should take proactive measures to introduce financial instruments designed to optimize investment efficiency, thus facilitating eco-friendly projects.

To the best of the authors’ knowledge, this paper holds the distinction of being the first to examine the impact of a specific type of green bond, namely, PCBs, on overinvestment. Furthermore, it contributes to the literature on personality traits, particularly within the context of the upper echelon theory, by investigating the moderating influence of CEO overconfidence.

This study aims to investigate the external effect of the economic growth target pressure of local governments on establishment-level SO₂ emissions.

Based on manually collected panel data of 74,058 China's industrial establishments and more than 330 thousand observations from CIED and ESR, the authors use a firm-fixed effect model, instrumental variables estimation and heterogeneity tests to identify the environmental externality of economic growth target pressure.

The establishments in cities that meet or slightly exceed the economic growth target experience greater negative externality measured by SO₂ emission intensity. This external effect is more pronounced in regions: with a strict and overweighted target setting; with stronger officials' promotion incentives; with a low degree of marketization; and in firms with great economic importance. The authors identify the underlying mechanisms of dependence on dirty industry and the relaxation of environmental enforcement. And the environmental protection constraints in 2007 mitigate the negative externality.

The paper sheds light on to what extent economic growth target pressure has a negative externality of pollution in China and how this pressure may conflict with environmental protection.

This paper complements prior research on the economic effects of economic growth targets, expands the knowledge on the determinants of establishment-level pollution emission from the perspective of target pressure and provides insight into the environmental externality that results from political factors.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

This paper aims to analyze factors based on organizational knowledge management (KM; transactional memory systems and knowledge-oriented leadership [K-OL]) that help firms to mitigate conflicts based on task management at work, with the aim to improve their innovation capabilities (IC). The knowledge-based view of the firm, conflict management theory and cognitive collective engagement theory have been used to build a model of relationships that connects the development of positive KM contexts and management of dysfunctional conflict with IC improvement.

Data survey collected from inland hotel establishments in Spain is used to test seven hypotheses by means of structural equations modeling, applying the partial least squares technique. Direct, indirect and mediating relationships between variables are examined from the structural path model.

The results confirm that, as expected, IC improve when K-OL and transactive memory systems (TMSs) are properly implemented by hotel establishments, which leads them to reduce negative effects of task management conflict (TMC). Significant direct effects are found between the key variables of the study and also a significant indirect effect between K-OL and IC through TMS reinforcement and the mitigation of TMC.

This paper provides useful ideas for hotel managers about how to improve KM contexts in their establishments while avoiding TMC. Efforts devoted to creating those contexts by hotel establishments are shown to be effective to improve their IC and create competitive advantages.

The analysis of IC improvement by studying TMC mitigation had not been researched to date by the KM literature. The consideration and testing of a model that integrates KM-related tools such as K-OL and TMS to avoid TMC in the hotel industry is the main contribution of this study.

This study aims to answer the following research question: What are the opportunities for future research concerning the Frugal Innovation (FI) phenomenon? To address this, the authors propose a novel approach to literature review on the topic. They do so in view of synthesising scholars’ recommendations for subsequent studies. They also advocate that it is time to contribute to the establishment of the FI field by mapping the future of this approach.

The authors conducted a systematic literature review (SLR) to connect past and future research on FI. After the screening process of the documents extracted from multiple databases, they performed a bibliometric analysis to provide an overview of the field. Furthermore, the lexical analysis and descending hierarchical analysis were generated through the IRAMUTEQ software to identify the clusters for future research on FI.

This research not only demonstrates the current state of the art of FI literature but also identifies a research agenda with six categories of opportunities for further studies on the topic: frugal consumer behaviour; establishment of the field; sustainable impact; approaches to different contexts; implementation processes; and challenges for value creation.

The FI phenomenon is receiving increasing attention from scholars in the management field due to its socioeconomic and managerial implications, especially after the Covid-19 outbreak. Therefore, the findings benefit scholars striving to expand the scope of FI research, as well as entrepreneurs, managers and organisations aiming to enhance their social responsibility to reduce their environmental impact.

This study aims to explore the dissemination and evolutionary path of online public opinion from a crisis management perspective. By clarifying the influencing factors and dynamic mechanisms of online public opinion dissemination, this study provides insights into attenuating the negative impact of online public opinion and creating a favorable ecological space for online public opinion.

This research employs bibliometric analysis and CiteSpace software to analyze 302 Chinese articles published from 2006 to 2023 in the China National Knowledge Infrastructure (CNKI) database and 276 English articles published from 1994 to 2023 in the Web of Science core set database. Through literature keyword clustering, co-citation analysis and burst terms analysis, this paper summarizes the core scientific research institutions, scholars, hot topics and evolutionary paths of online public opinion crisis management research from both Chinese and international academic communities.

The results show that the study of online public opinion crisis management in China and internationally is centered on the life cycle theory, which integrates knowledge from information, computer and system sciences. Although there are differences in political interaction and stage evolution, the overall evolutionary path is similar, and it develops dynamically in the “benign conflict” between the expansion of the research perspective and the gradual refinement of research granularity.

This study summarizes the research results of online public opinion crisis management from China and the international academic community and identifies current research hotspots and theoretical evolution paths. Future research can focus on deepening the basic theories of public opinion crisis management under the influence of frontier technologies, exploring the subjectivity and emotionality of web users using fine algorithms and promoting the international development of network public opinion crisis management theory through transnational comparison and international cooperation.