Search results

This study aims to investigate how a firm’s sustainable competitive advantage is influenced by the combination of entrepreneurial ecological orientation, digital transformation and dynamic capabilities.

Based on the qualitative comparative analysis method, this study systematically explores the significant key conditions and configuration effects that affect the growth of sustainable competitive advantage. This study uncovers the causal relationship and complex mechanisms underlying the sustainable and unsustainable competitive advantages for new ventures, by examining the grouping effects of the above three factors – entrepreneurial ecological orientation, digital transformation and dynamic capabilities on those advantages.

A single factor fails to constitute a necessary condition for sustainable competitive advantage. Three types of configurations are beneficial to sustainable competitive advantage, namely, flexible and responsive type, dynamically adjusted type and type of opportunity resource integration, whereas four types of configurations lead to the unsustainable competitive advantage of new ventures, namely, type of organizational rigidity, informal entrepreneurial type, information-blocking type and technology-deficient type.

According to this study, adopting an entrepreneurial ecological orientation is a novel strategic move. This study offers an extensive review of three aspects of entrepreneurial ecological orientation, dynamic capacities and digital transformation and their mutually synergistic cascading effects on the sustainable competitive advantage of new ventures. This study investigates how three dimensions interact to achieve sustainable competitive advantage for firms, ultimately contributing to the study of sustainable competitive advantage strategies from an entrepreneurial ecosystem perspective.

The purpose of this study is to present a system dynamic (SD)-based remanufacturing economic analysis model of used automobile engine under two recycling modes. The authors will compare the remanufacturing cost, sales profit and sales revenue from time and space dimensions incurred in different recycling modes in the long run.

The remanufacturing economic analysis model is based on SD methodology. The authors can simulate the relations of impact factors on automobile engine recycling and remanufacturing and further analyze and compare the cost, sales profit and sales revenue incurred in different recycling modes in the long term.

Sinotruk Steyr engine remanufacturing in Shandong province is taken as the research case subject. The revenue, cost and profit under the two recycling modes from 2015 to 2035 are analyzed and compared. The results show that different recycling modes have significant varying influence on the economy of engine remanufacturing.

This economic analysis model can provide a method reference to decide the recycling mode for auto components and other product remanufacturing. Moreover, this model can guide and support the sustainable development of remanufacturing industry.

It is a complex and dynamic process to provide high-quality rural infrastructure. However, there lacks a holistic performance evaluation method for rural infrastructure provision that reflects changing rural social needs and takes a village as a whole. This study aims to develop a holistic and dynamic performance evaluation model for rural infrastructure in Mainland China.

This study established an evaluation index system by combining the lifecycle theory and the economy, efficiency, effectiveness and equity (4E) theory. This study developed an evaluation model by using the analytic network process (ANP) and matter-element analysis theory (MEAT). The model was validated by two representative villages in Mainland China.

The developed model can reflect dynamic social needs and effectively evaluate the overall infrastructure provision performance of a village. The weight of indicators reflects the changes in Mainland China’s contemporary rural social needs, with particular emphasis on the impact and output performance. The evaluation result shows that the overall performance of the representative villages was excellent but had a tendency toward good. Although the output performance was excellent, different input, process and impact performances resulted in different downgrade trends.

This study provides a theoretical basis for disaggregating the complex issue of the performance of rural infrastructure provision. The results can be used by relevant authorities to make a holistic and dynamic evaluation of the performance of rural infrastructure provision and timely revise planning and management policies.

This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

The purpose of this paper is to propose a method that can calculate the transportation infrastructure network service capacity enhancement given by planned transportation infrastructure projects construction. Managers can sequence projects more rationally to maximize the construction effectiveness of infrastructure investments.

This paper designed a computational network simulation software to generate topological networks based on established rules. Based on the topological networks, the software simulated the movement path of users and calculated the average travel time. This software allows the adjustment of parameters to suit different research objectives. The average travel time is used as an evaluation index to determine the most appropriate construction sequence.

In this paper, the transportation infrastructure network of Sichuan Province in China was used to demonstrate this software. The average travel time of the existing transportation network in Sichuan Province was calculated as 211 min using this software. The high-speed railways from Leshan to Xichang and from Xichang to Yibin had the greatest influence on shortening the average travel time. This paper also measured the changes in the average travel time under two strategies: shortening the maximum and minimum priorities. All the transportation network optimisation plans for Sichuan Province will be somewhere between these two strategies.

The contribution of this research are three aspects: First, a complex network analysis method that can take into account the differences of node elements is proposed. Second, it provides an effective tool for decision makers to plan transportation infrastructure construction. Third, the construction sequence of transportation infrastructure development plan can effect the infrastructure investment effectiveness.

This study aims to investigate the rarefaction effects on flow and thermal performances of an equivalent sand-grain roughness model for aerodynamic thrust bearing.

In this study, a model of gas lubrication thrust bearing was established by modifying the wall roughness and considering rarefaction effect. The flow and lubrication characteristics of gas film were discussed based on the equivalent sand roughness model and rarefaction effect.

The boundary slip and the surface roughness effect lead to a decrease in gas film pressure and temperature, with a maximum decrease of 39.2% and 8.4%, respectively. The vortex effect present in the gas film is closely linked to the gas film’s pressure. Slip flow decreases the vortex effect, and an increase in roughness results in the development of slip flow. The increase of roughness leads to a decrease for the static and thermal characteristics.

This work uses the rarefaction effect and the equivalent sand roughness model to investigate the lubrication characteristics of gas thrust bearing. The results help to guide the selection of the surface roughness of rotor and bearing, so as to fully control the rarefaction effect and make use of it.

Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.

To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.

The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.

The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

This study examines how structured procedures and psychological safety can enhance six sigma processes’ effectiveness and foster individual creativity within an organization.

This study employs a novel methodological approach, combining structural equation modeling (SEM) and multi-value qualitative comparative analysis (mvQCA).This combination gave us a unique lens through which to view the complex connections between the constructs that were the subject of the investigation.

The results substantiate that a well-structured six sigma approach, paired with a psychologically safe environment, fosters knowledge exploration and exploitation as well as triggering conducive learning behaviors. These elements, in turn, augment individual creativity, which is found to be instrumental in boosting six sigma performance.

This study is one of the first to combine mvQCA and SEM to investigate the complex interplay between structured methods, psychological safety and individual creativity in enhancing six sigma performance. This research highlights the importance of fostering creativity in strategic management processes and the unrealized potential of combining these methods.

Globally, the landscape of corporate carbon disclosures (CCD) is continually evolving as societal, environmental and regulatory expectations change over time. The goal of this study is to examine the challenges faced by Indian firms’ corporate carbon reporting (CCR). The literature recognized the hurdles to reaching net zero emissions and decarbonization, which are equally applicable to carbon disclosure (CD).

The scope 3 emission disclosure barriers (S3EDBs) identified from the literature were ranked, and their relationships were discovered using the “Grey-based decision-making trial and evaluation laboratory” (Grey- DEMATEL) technique.

The key findings are the S3EDBs, the most prominent barriers, their interrelationships and important insights for managers of organizations in prioritizing the action area for scope 3 CD. Eight S3EDBs were categorized in terms of cause and effect, threshold value is calculated as 0.78. “Quality, and reliability of data,” “Government policies and statutory requirement on emission disclosure” and “Traceability and managing supply chain partners” are the most prominent S3EDBs.

The results will help industry people in countries with emerging economies that have significant scope 3 carbon footprints. The managers can plan to deal with top S3EDBs as a step towards decarbonization and ultimately fighting climate change (CC).

This study is one of the first to rank these barriers to CD so that industry practitioners can prioritize their actions. The core contribution of this research is to detect the most significant S3EDBs and their interdependencies.

This study aims to determine the impact of corporate digital transformation on the audit opinions of auditors in A-share nonfinancial listed companies in China. It also examines how corporate internal control and corporate social responsibility (CSR) disclosure levels moderate this effect. This study fills a gap in the literature by investigating the impact of digital transformation on business performance, especially in the Chinese context, where digital transformation is rapidly progressing. This study also offers practical guidance for practitioners on whether and how to undergo a digital transformation and enhance their internal governance and social responsibility practices.

This study uses a sample of 2,637 Chinese A-share nonfinancial listed companies from 2009–2022, after excluding firms with ST, ST* or PT status; negative revenue; and missing data for three or more consecutive years. Digital transformation index data is collected from firms’ annual reports, and the other microlevel data from the Wind and CSMAR databases. The authors winsorize the data at 1% for outliers, resulting in 17,305 firm-year observations. This study uses fixed-effects logistic regression with clustered robust standard errors to analyze the binary dependent variable. This study also performs various robustness checks, such as probit model, multilevel fixed effects model and IV 2SLS estimations, to confirm the validity of the results.

This study reveals that digital transformation leads to standard unqualified audit opinions, meaning that companies that invest more in digital technologies and capabilities has more tendency to receive standard unqualified audit opinions, which signify the reliability and credibility of their financial reporting. This study also finds that corporate internal control and CSR disclosure levels positively moderate the effect of digital transformation on audit opinions. This study further conducts heterogeneity analysis and shows that the positive effect is originated by the state-owned enterprises, firms audited by non-Big4 auditing firms, firms with high internal control levels and firms with low CSR disclosure levels. The results are robust to different econometric methods.

This study contributes to the literature by providing empirical evidence on how digital transformation influences audit quality and credibility and how internal governance and social responsibility practices strengthen this influence. This study also has practical implications for practitioners by providing advice on whether and how to pursue a digital transformation and improve their internal governance and social responsibility practices. This study demonstrates its originality by reviewing the existing literature from three theoretical perspectives: stakeholder, signaling and reputation, and identifying the research gap that the study addresses. This study also compares its findings with previous studies and discusses the implications and limitations of its research. This study also proposes directions for future research based on its findings.