Systems, Volume 12, Issue 5 (May 2024) – 19 articles

The purpose of this study was to establish a spatial structural framework to explore how cross-border mergers and acquisitions (M&As) in emerging markets can enhance long-term productivity and select the appropriate host country market structures. Utilizing cross-border M&A data from Chinese companies from 2008 to 2016, we developed a moderated U-shaped mediation model. Employing Two-Stage Least Squares and the Generalized Method of Moments for endogeneity analysis, we offer robust empirical insights. Our findings illustrate that enterprise productivity progression from cross-border M&As is significantly influenced by a U-shaped mediation of the host country’s market size effect, which is further moderated by the technological distance between the home and host countries. A high technological distance intensifies the U-shaped mediation of market size effects on enterprise productivity, while low technological distances result in an inverted U-shaped curve, indicating that such markets may boost short-term productivity but limit long-term growth. Conversely, larger markets with greater technological distances better support sustained productivity increases, even requiring persistent technological absorption efforts. This study underscores the necessity of selecting appropriate host country market structures and effectively managing the acquisition timeline to positively impact both short- and long-term productivity. By conceptualizing firm-level technological absorption efforts as the technological gap between the home and host countries, this study highlights the crucial moderating role that the technological gap plays in influencing long-term productivity at the macro level, providing new insights into the economic geographic strategic decisions and spatial planning for emerging market enterprises in cross-border acquisitions. Full article

Cardiovascular diseases (CVDs) represent the leading cause of death globally. Romania recorded the highest mortality rate due to CVDs in the EU in 2022, with 162,984 deaths, while the number of registered patients with CVDs surpassed 4 million. This study aims to measure the population’s potential spatial accessibility to cardiovascular hospitals in Romania, as timely access to such healthcare facilities is crucial to minimise avoidable mortality due to CVDs. Although distance is an essential parameter of spatial accessibility, time-based analysis is more reflective of real-world scenarios due to the unpredictability of travel. The potential spatial accessibility was measured using the Application Program Interface (API) offered through the Google Maps platform and a personal car as the transportation mode. The country’s cardiovascular hospital network comprises 161 units, of which 84 can provide complex care. Because all of them are located in urban areas, three different time slots were considered to distinguish between high and low traffic congestion situations. We created hierarchies of ten-minute and five km intervals for travel time and distance, respectively, to emphasize the population percentages with better or low potential spatial accessibility. Results showed that only 15% of the population can reach the nearest cardiovascular hospital in less than 20 min, and 23% must travel for over 60 min, while 45.7% live farther than 20 km from a cardiovascular hospital. Inhabitants living in remote areas, especially rural ones, are the most vulnerable, having to travel for the longest time and distance. Actions like improving the existing transport infrastructure and upgrading healthcare facilities and equipment are needed to ensure better medical care and an adequate response to population needs. This study can support local authorities in optimising spatial accessibility to cardiovascular care by identifying the most burdened hospitals in the context of low medical specialised staff and large catchment areas. Full article

Institutional, policy, and management scholars and practitioners are increasingly interested in leveraging network perspectives, methods, and data to understand complex social phenomena, including the various stages of the policy process, community mobilization, and coupled natural and human systems. Viewing these phenomena through the lens of system-oriented networks can be valuable for understanding and intervening within complex policy arenas. However, currently, there is no clear consensus on who and what constitutes a relevant actor in a system-oriented network. Furthermore, numerous conceptual and methodological traditions for conceptualizing, measuring, and analyzing system-oriented networks have arisen, and each is linked to different disciplinary traditions. In this paper, we showcase six approaches from the public policy and public management literature for conceptualizing and analyzing system-oriented networks. We offer a conceptual framework for characterizing different approaches which considers differences in their focal system of interest, analytical focus, theoretical orientation, and approach for determining network boundaries. We review these elements with an eye toward helping scholars and practitioners interested in system-oriented networks to make informed decisions about the array of available approaches. Full article

This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a Portuguese cold chain logistics company was selected for the analysis of the allocation of the products in the warehouse and of the corresponding outbound movements. Data with 8525 movements that occurred during 2021 were collected for 228 different product references. For this case study, the products that had a picking place in the initial scenario now have pallets with all the goods in the reserve area, and vice versa. The mathematical models were permitted to obtain savings for the logistics operator costs of around 30.9%. The proposed models can, in the future, be applied in other warehouse scenarios to companies in completely different sectors of activity. Full article

Population migration continues to reshape the spatial pattern of China’s population and regional economic development. During this internal migration process, production and consumption patterns often change, ultimately leading to changes in green total factor productivity. This paper, based on the Chinese population census data and 1% sampling survey data from 2005 to 2015, utilizes social network analysis methods to measure the labor mobility network indicators of 284 prefecture-level cities. Further, this paper analyzes the impact and mechanisms of regional network status on green total factor productivity using a panel fixed effects model. We find that as network density increases, the interpersonal connections between regions become closer, and the network exhibits a clear pattern of “concentrated inflows” and “dispersed outflows”, with the trend of forming strong alliances becoming increasingly apparent. Regions positioned centrally either in terms of network in-degree or out-degree exhibit higher green total factor productivity. Among these, the labor mobility network plays a crucial role in enhancing green total factor productivity through the channel of technology diffusion effects, which improve investment efficiency via knowledge exchange and material capital accumulation. The promotive effect of labor network status on green total factor productivity is more pronounced in the eastern regions, where talent quality is higher, and in areas with fewer restrictions from the household registration system. Full article

The healthcare industry is confronted with the challenge to offer an increasing variety of healthcare services while in the meantime controlling rapidly increasing healthcare costs. Mass customization has been proven to be an effective strategy to fulfill customers’ individual specific needs with high efficiency and low cost in the manufacturing industry. This paper investigates the theoretical feasibility and practical applicability of adopting mass customization as a conceptual framework for designing a healthcare service delivery system. The nature of healthcare delivery systems and their evolution are discussed relative to those of manufacturing systems. Recent research in personalized medicine, consumer-driven healthcare, consumer healthcare informatics, and integrated healthcare delivery is reviewed as enabling technologies towards mass customization of healthcare services. By synthesizing these scattered efforts in different streams of literature, this paper concludes that mass customization can contribute to the redesign of healthcare service systems, and delineates a roadmap for future research. Full article

Scientific experiments cover a wide range of fields—from basic to applied scientific research. Chemical experiments are the basis for cultivating chemical knowledge in scientific experiments and are an important way to cultivate scientific thinking and methods. However, due to the toxicity or flammability of the chemical substances in the experiments, hazardous events often lead to personal injuries and environmental damage. Exactly assessing risk factors and reducing the risk of hazards to protect the experimenters and ensure environmental safety are crucial in chemical experiments. However, while the traditional risk evaluation method cannot consider the weight of risk evaluation criteria, it also cannot effectively address problems through hierarchical analysis, as well as imprecise and ambiguous information inherent in human cognition. Therefore, this paper proposed an approach based on failure mode and effects analysis (FMEA) to assess the risk of chemical experiments in a fuzzy information environment. The approach combines the typical analytic hierarchy process (AHP), the risk priority number (RPN) of FMEA, and the intuitionistic fuzzy set (IFS) methods to evaluate risks associated with chemical experiments and consider the damage recovery in chemical experiments. This study applied the case of a university chemistry experiment, “preparation of hydrogels”, to validate the reasonableness and correctness of the proposed approach and compare its numerical verification results with those from the typical RPN, the AHP-RPN, and the AHP-fuzzy risk priority number (AHP-FRPN) methods. The finding demonstrates that the proposed method can more effectively address risk evaluation problems in chemical experiments than the other methods. This result serves as an important reference for reducing chemical experiment risk occurrences. Full article

Facing the increasingly complex and uncertain external environment, the reasonable control of investment risk is the key to realizing the sound operation and high-quality development of enterprises. Based on the innovation perspective, this paper takes A-share non-financial listed companies from 2007 to 2021 as the research sample to explore the impact of the corporate risk-taking level on the high-quality development of enterprises and examines the transmission mechanism of the relationship between the two from the perspectives of innovation efficiency, innovation input, and innovation output. It is found that enterprise risk-taking significantly inhibits the high-quality development of enterprises, by reducing innovation efficiency; innovation efficiency plays a mediating role in the influence of the relationship between the two, which is mainly due to the fact that enterprise risk-taking increases the innovation inputs of enterprises but reduces the innovation outputs of enterprises, and then reduces the innovation efficiency of enterprises and inhibits the high-quality development of enterprises. This heterogeneity study finds that the inhibitory effect of corporate risk-taking on the high-quality development of firms is more pronounced among manufacturing firms, small-scale firms, and firms with higher capital intensity. The findings of this study provide both guidance to help enterprises to reduce risky investment decision-making behaviors and experience for regulators to effectively promote the formulation of policies related to the high-quality development of the real economy. Full article

In this paper, we consider a multiperiod, two-location inventory system with unknown demand distributions and perishable products. Products can be transshipped from the location with excess inventory to the other with excess demand to better fulfill customer demand. The demand distributions are assumed to follow a family of parametric distributions and can only be learned on the fly. To address the challenge, we propose a data-driven inventory management algorithm called DD2LI that achieves a good performance in terms of regret. This algorithm, DD2LI, employs maximum likelihood estimation to approximate the unknown parameter and determines the order quantity based on these estimations. In addition, we emphasize a key assumption that tightens regret bound. Finally, we test the effectiveness of our proposed algorithm by conducting numerical experiments for two scenarios. Full article

During the 2008 financial crisis, central banks (such as the Fed) adopted a quantitative easing (QE) policy to stimulate their countries’ economies and overcome severe economic and financial recessions. However, apart from stimulating the economy by issuing a substantial amount of currency to purchase long-term bonds and suppress interest rates, QE policy also contributed to a boom in the real estate and construction sectors. Therefore, this study employs data envelopment analysis to measure the business performance (BP) of construction companies, and explore the impact of QE policy on the BP of construction companies, between 2004 and 2015, using hierarchical regression. We also examine the moderating role of the debt ratio on the relationship. Focused on publicly listed construction companies in Taiwan, this research reveals three encouraging findings. Firstly, QE policy indeed enhanced the BP of Taiwanese construction companies. Secondly, performance improvements in construction companies due to QE policy show a time-diminishing trend, suggesting the importance of seizing the initial policy benefits of QE implementation. Lastly, construction companies with appropriate financial leverage may exhibit better BP. These findings can provide valuable insights for relevant government entities and decision-makers in the industry for policy and investment decisions. Full article

The past COVID-19 pandemic introduced the world to the necessity of dealing with the trade-off between minimizing probability of contagion, and providing people with services they need. This trade-off stipulates that a large person-to-person distance will reduce contagion probability, but will render service inefficient, and vice versa. This work focuses on the urban rail transit (URT) hub, as an example of a busy passenger area, from which we can derive an optimal preparedness policy to use during the pandemic time of any coronaviruses. We use simulation methodology, based on the classical social force model, to represent behaviors and characteristics of pedestrians. Passenger flow movement process is a mechanism we explore to figure out how the epidemic management policy and pedestrian psychological-related behaviors interact with the URT system. The systems’ complexity regarding contagion-prevention distances are tested over a few scenarios: before/after the outbreak, and for different person-to-person distances demonstrating different crowd levels. A case study of Xinjiekou Station, Nanjing URT, China, enables assessment of passenger management policy with person-to-person distances of 0.5 m, 1.0 m and 2.0 m. Multi-scenario performance illustrates the trade-off in dynamic between the efficiency of pedestrians’ walking behaviors and the distancing needs for preventing coronaviruses transmission. The results show that queuing length with social distancing of 1.0 m and 2.0 m is increased by 4.17% and 21.22%. The average delays in boarding are 14.1 s and 22.5 s for 1.0 m and 2.0 m, which leads to 15.29% and 22.39% increases, respectively, in comparison with ordinary social distancing of about 0.5 m. Full article

Considering the characteristics of both quality and quantity losses in fresh produce as well as the existence of spot markets, optimal retailer ordering, pricing, and freshness-keeping decisions through the single ordering policy (firm ordering only or option ordering only) and the mixed ordering policy (firm ordering and option ordering simultaneously) are constructed based on option contracts and analyzed for the retailer under different ordering policies. The results show that there is a unique optimal pricing, ordering, and freshness-keeping decision under all three ordering policies, but there is no joint decision. The optimal freshness-keeping and retail price under the mixed ordering policy are lower than those under the option ordering only but higher than those under the firm ordering only. When only a single order can be placed, the retailer’s optimal ordering policy is determined by demand risk. When all three ordering policies are available, the optimal ordering policy for the retailer is the mixed ordering policy. A spot market will weaken the role of option contracts in mitigating supply chain risks, and the larger the risk, the more significant the role of the spot market. Full article

Human skills are a critical factor in the success or failure of a digital project. Limited studies have been conducted to identify the industry demand for skills of scrum roles (product owner, scrum master, web developer) and levels (entry, associate, mid-senior). The evaluation of skills over time benefits both decision-makers and associated team members, which leads to successful project completions. The aim of this research is to improve decision making concerning the level-specific skills of selected scrum roles for digital projects. The study identifies major and minor skills, patterns, and relationships between levels, and formulates the mathematical equations as the most important inputs to the skill-driven model’s implementation and evaluation. Both qualitative and quantitative research methods were used to analyse 900 surveyed job advertisements published on LinkedIn in Europe. Descriptive analysis was used to analyse quantitative data while the deductive approach was followed with thematic analysis. There are required skill sets for each level of roles, level-specific skills, industry-demanded skills, and formulas related to the initial and individual skill ratings that are investigated. A new mechanism for evaluation is introduced based on “the time spent with skills”. As a result, the proposed model is implemented by feeding research findings into the Mendix programming platform. The skill-driven model is a decision-support solution in software project management to evaluate skills which assist in assigning the right person to the right digital project. Further investigation on different job portals can help to improve the accuracy of industry standards and reduce the lack of progression skills by overcoming limitations identified in this paper. Full article

With climate warming, the human living environment faces significant challenges, and global environmental protection and sustainable development are accelerating. As a result, ESG has become an essential area of research. This study explores the impact of employees’ perceptions of corporate ESG performance on green innovation, focusing on the moderating role of digital transformation. A survey was conducted among 316 employees from the wholesale, retail, IT, and computer services industries to validate this study. Research results show that employees’ cognitions of corporate ESG performance have a positive impact on green innovation. In addition, digital transformation plays a positive moderating role in the impact of the environmental (E) and social (S) dimensions of ESG performance on green innovation. These findings not only highlight the critical role of personal awareness and ESG management concepts in future corporate strategies but also indicate the importance of the extent of digital transformation in companies to improve innovation performance. Full article

The implementation of prefabricated housing (PH) has become prevalent in China recently due to its advantages in enhancing production and energy-saving efficiency within the construction system. However, stakeholders may not always fully realize the benefits of adopting PH due to the emergence of transaction costs (TCs) in the development process of such projects. This study investigated the strategies for developers to make rational choices for minimizing the TCs of the PH project considering their own attributes and external constraints. A Bayesian Belief Network model was applied as the analytical method, based on surveys conducted in China. A single sensitivity analysis indicated that developers influence the TCs of PH through the following three most impactful factors: prefabrication rate, PH experience, and contract payment method. Integrated strategies are recommended for developers in various situations based on a multiple sensitivity analysis. Developers facing challenges due to high prefabrication rates are advised to reduce the risks by procuring highly qualified general contractors and adopting unit-price contracts. For developers with limited PH experience, adopting the Engineering–Procurement–Construction procurement method is the most efficient way to reduce their TCs in the context of China’s PH market. This study contributes to the current body of knowledge concerning the effect of traders’ attributes and choices on TCs, expanding the application of TC theory and fulfilling the study on the determinants of TCs in construction management. Full article

Corporate social responsibility (CSR) extends beyond mere profit-seeking to encompass the ethical behavior of a company toward society, mitigating negative and generating positive impacts on the environment, consumers, employees, communities, and all stakeholders. The UN Global Compact (UNGC) is the world’s largest voluntary CSR initiative, and its Communication on Progress (CoP) requirement is a key reporting mechanism that allows participating companies to transparently showcase their progress and efforts regarding CSR. As more and more companies are reporting CSR practices, it is crucial to establish a global, standardized, trusted, accessible, and useful database that can be used by different stakeholders, including the companies themselves in the benchmarking process. This paper examines whether the UNGC CoP can be used as a sustainability benchmarking tool, based on well-established criteria, and compares it with other existing reporting frameworks. Results indicate that the UNGC CoP can be considered a benchmarking tool, being applicable to nearly all phases of the benchmarking process. The study also shows that the CoP stands out regarding other frameworks due to ample coverage of the sustainable development goals (SDGs), number of reporting companies, accessibility to all stakeholders, and consolidation of the information into one platform. Full article

Background: Our research delved into exploring various selected facets of AI-driven employee engagement, from the gender perspective, among Slovenian entrepreneurs. Methods: This research is based on a random sample of 326 large enterprises and SMEs in Slovenia, with an entrepreneur completing a questionnaire in each enterprise. Results: Findings suggest that there are no significant differences between male and female entrepreneurs in Slovenia regarding various aspects of AI-supported entrepreneurial management practice including the following: AI-supported entrepreneurial culture, AI-enhanced leadership, adopting AI to reduce employee workload, and incorporating AI tools into work processes. The widespread integration of AI into entrepreneurship marks a transition to a business landscape that values inclusivity and equity, measuring success through creativity, strategic technology deployment, and leadership qualities, rather than relying on gender-based advantages or limitations. Our research also focused on the identification of gender differences in path coefficients regarding the impact of the four previously mentioned aspects of AI on employee engagement. While both genders see the value in using AI to alleviate employee workload, the path coefficients indicate that female entrepreneurs report higher effectiveness in this area, suggesting differences in the implementation of AI-integrated strategies or tool selection. Male entrepreneurs, on the other hand, appear to integrate AI tools into their work processes more extensively, particularly in areas requiring predictive analytics and project scheduling. This suggests a more technical application of AI in their enterprises. Conclusions: These findings contribute to understanding gender-specific approaches to AI in enterprises and their subsequent effects on employee engagement. Full article

The creation of innovations in enterprises is a topic that has a firm place in the scientific literature, and the authors describe several procedures and theories of the functioning of the innovation process in enterprises. One part of the innovation process is decision-making. In the article, attention is paid to the results of research on the decision-making process as an important and integral part of the innovation process. The elements of the decision-making process and their impact on innovation represent an area of possible exploration and development of theories on decision-making in the innovation process. It is here that we have proposed a methodology for decision-making in the innovation process. The management of innovation processes, of which decision-making is an important part, provides multiple points of error for enterprises. Errors are experienced by enterprises due to a lack of information or its misuse, the influence of communication and time, and also the lack of experience of employees. The aim of this paper is to highlight the different decision-making criteria and approaches to innovation activities in Slovak enterprises within the framework of the developed methodology and survey results. Full article

This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for predicting stock price crash risk, with relatively satisfactory performance. Meanwhile, the SHapley Additive exPlanations (SHAP) method is used to interpret the importance of features. The results show that the average weekly return of a firm over a year (RET) contributes the most and is negatively associated with crash risk, followed by Sigma, IPO age, and firm size. We also found that, among CEO characteristics, CEO pay contributes substantially to crash risk at the firm level. Our findings have important implications for research into the impact of firm and CEO characteristics on stock price crash risk and provide a novel way for investors to plan their investment decisions and risk-taking behavior rationally. Full article