Stakeholder management researchers have recently put a lot of effort into figuring out why organizations facing extensive pressure respond differently to social responsibilities. In particular, ethics researchers believe that senior management must drive corporate social responsibility since their attitudes toward such issues are so important. In line with this sentiment, our study develops a framework of management power, composed of CEOs’ power and the organizations’ power, and explores how managerial power heterogeneity affects the corporate social responsibility (CSR) performance of a firm. Using sample data from the largest emerging market—China—for the period 2010–2018, we submit that CEOs with structural power and shareholders with the highest concentration tend to show a lower commitment to CSR activities. On the other hand, we recognize that the ownership, expertise, and prestige power of CEOs’, the supervision, monitoring, and political power of the board can improve a firms’ CSR performance. These results are also validated by using a fixed effect model, two stage least square (2-SLS) regression, and the propensity score matching (PSM) technique. Our results imply that the implementation of social policies fundamentally results not only from powerful CEOs, but also from powerful boards and shareholders. Moreover, our study provides useful implications with regard to the social outcomes of power authorized by CEOs and the organizations.
The purpose of this research was to analyze how different effects of the COVID pandemic, expressed through pandemic accentuated occupational stress, perceived job insecurity, occupational safety and health perception and perceived organizational effectiveness, may impact turnover intentions of the personnel in the hospitality industry. Our research team designed an online questionnaire which was analyzed with network analysis to depict the relationship between factors, and, then, a confirmatory factor analysis was employed to confirm the distribution of the items to the envisaged five factors. Based on a sample of 324 randomized Romanian hospitality industry staff, the results of our cross-sectional study revealed that occupational safety and health perception, perceived organizational effectiveness and perceived job insecurity in the pandemic accentuated occupational stress to indirectly and significantly impact hospitality industry staff turnover intentions (TI). The results indicated that, while the total effect of PAOS on TI was significant, the direct effect was still significant, while all three mediators remained significant predictors. Overall, mediators partially mediated the relationship between PAOS and TI, indicating that employees with low scores on occupational safety and health perception (OSHP), and perceived organizational effectiveness (POE) and high scores on perceived job insecurity (PJI) were more likely to have higher levels of TI turnover intentions.
A bean counter is defined as an accountant or economist who makes financial decisions for a company or government, especially someone who wants to severely limit the amount of money spent. The rise of the bean counter in both public and private companies has motivated us to develop a Bean Counter Profiling Scale in order to further depict this personality typology in real organizational contexts. Since there are no scales to measure such traits in personnel, we have followed the methodological steps for elaborating the scale’s items from the available qualitative literature and further employed a cognitive systems engineering approach based on statistical architecture, employing cluster, factor and items network analysis to statistically depict the best mathematical design of the scale. The statistical architecture will further employ a hierarchical clustering analysis using the unsupervised fuzzy c-means technique, an exploratory factor analysis and items network analysis technique. The network analysis which employs the use of networks and graph theory is used to depict relations among items and to analyze the structures that emerge from the recurrence of these relations. During this preliminary investigation, all statistical techniques employed yielded a six-element structural architecture of the 68 items of the Bean Counter Profiling Scale. This research represents one of the first scale validation studies employing the fuzzy c-means technique along with a factor analysis comparative design.
The purpose of this study is to examine the factors that influence vaccination options, including vaccination against COVID-19, in order to develop a management algorithm for decision-makers to reduce vaccination reluctance. This paper’s primary objective is to empirically determine the relationships between different variables that correlate to non-vaccination behavior of the target population, as well as the implications for public health and situational management strategies for future vaccination intentions. We created a questionnaire to investigate the personal approach to disease prevention measures in general and vaccination in particular. Using SmartPLS, load factors for developing an algorithm to manage vaccination reluctance were calculated. The results shows that the vaccination status of an individual is determined by their vaccine knowledge. The evaluation of the vaccine itself influences the choice not to vaccinate. There is a connection between external factors influencing the decision not to vaccinate and the clients’ motives. This plays a substantial part in the decision of individuals not to protect themselves by vaccination. External variables on the decision not to vaccinate correlate with agreement/disagreement on COVID-19 immunization, but there is no correlation between online activity and outside influences on vaccination refusal or on vaccine opinion in general.
Sustainable urban development has come to play an essential role in establishing and growing future sustainable cities, or smart cities, which are urban areas that have an optimum carbon footprint, are nature-friendly, and are smart enough to enhance energy efficiency. This study is based on qualitative research in which data were collected from interviews with real estate development specialists. The interviews were addressed to a total of 30 real estate developers from Romania between July and December 2022 and were conducted using the Zoom interface. The aim of this research was to analyze whether familiarity with the smart green concept influences the decision to implement the IoT on a large scale at the organizational level through the perception of specific determining factors in choosing the development of green building projects considering the operational costs. The results revealed a significant indirect effect of green building knowledge on large-scale IoT implementation through the mediator of the perception of operating cost factors, supporting our hypothesis. The direct effect in the presence of the mediator was not found to be significant anymore. Hence, there is full complementary mediation by the perception of operating cost factors on the relationship between green building knowledge and large-scale IoT implementation.
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