Social Reputation Research Articles

A New Approach to Assess Sustainable Corporate Reputation with Citizen Comments Using Machine Learning and Natural Language Processing

This study investigates the assessment of sustainable corporate reputation through citizen comments and how it can be measured by sentiment analysis methods based on machine learning and text mining. The research analyses citizen feedback on municipalities in the field of public services and examines their impact on the social reputation of the services provided by municipalities. Support vector machines, one of the machine learning methods, was used for sentiment analysis. In the study, Google Maps comments of the citizens receiving services from the municipality were used. The results of the sentiment analysis reveal that sustainable corporate reputation is directly related to citizen satisfaction and feedback. In this context, municipalities should continuously receive feedback and make strategic improvements based on citizens’ comments to ensure sustainable service quality. Municipalities are especially appreciated by citizens for their fast, effective, and high-quality services. However, some negative comments focus on issues such as the slowness of services, cleaning problems, and staff attitudes, indicating that certain improvements are needed. This feedback emphasises the need for continuous improvement in service quality.

Relationship Between ESG and Corporate Innovation Investment - Based by A-share Listed Companies

Abstract: ESG criteria are pivotal factors in assessing corporate performance, influencing not only a company's social reputation and investor trust but also its innovation investment decisions. Recognizing the crucial role of innovation in enterprise growth and its linkage to ESG factors, This essay examines the association between company innovation investments and ESG levels objectively. Utilizing China's A-shares as the research sample, an empirical analysis is conducted through a linear regression model. The findings conclusively reveal a notable correlation between ESG performance and the level of enterprise innovation investments. Specifically, companies with stronger ESG performance tend to invest more heavily in R&D and innovation. Conversely, enterprises with higher levels of R&D and innovation often exhibit superior ESG standards. This study underscores the mutual reinforcement between ESG practices and corporate innovation, emphasizing the importance of integrating ESG considerations into business strategies for sustainable growth and innovation.

Mreža občanske znanosti v Sloveniji

Citizen science is a concept of scientific research that promotes an open dialogue between the scientific community and the broader public and thus also guides social development. In Slovenia, this concept has developed considerably over the past year, in particular through the establishment of the specialised portal citizenscience.si and the Citizen Science Network. The European Union has also contributed to the promotion and visibility of citizen science mainly by funding. Along with other organizations as museums and societies, and individuals, universities and research institutes also make an important contribution to citizen science, not only by expanding and strengthening research activities through public engagement, but also by improving their social reputation and strengthening trust in science. In Slovenia, public participation in science has a long tradition, which went under various names until the term "občanska znanost" was established as the most appropriate translation. Since 2017, the Central Technical Library of the University of Ljubljana (CTK) has been involved in the promotion and development of citizen science in Slovenia, first by organising the expert meetings and later by directly supporting a citizen science project. They work with other organisations to organise events to promote collaboration between researchers and libraries and the general public, and is active in international organisations. The content of the citizenscience.si portal and an analysis of the published projects are presented in detail.

Labour rights protection and export expansion: Evidence from SA8000 certification

Protecting labour rights is an important approach to shaping the new advantages of trade competition and achieving high-quality development of China’s export trade. This paper considers SA8000 certification as the criterion for labour rights protection and manually collects information from firms certified by the National Certification and Accreditation Administration of China. Next, we investigate the effects of SA8000 certification on firm exports. The results show that SA8000 certification significantly expands the export scale while improving export quality. The mechanism analysis shows that the improvement effects are achieved in three ways: reducing operating costs, improving social reputation and increasing labour efficiency. Moreover, the positive effects of SA8000 certification are more profound for resource-intensive firms, non-state-owned firms and large firms. Our study provides an important reference for safeguarding labour rights and reconstructing new competitive advantages in exports.

Roots and rewards: Exploring the symbiotic relationship between familism and Chinese family business philanthropy

This study examines the profound influence of familism on the philanthropic behaviours of family businesses (FBs) in China, utilising data from A-share listed FBs from 2012 to 2020. Familism, a core cultural ethos in China, not only shapes family dynamics but significantly impacts broader socio-economic activities, including corporate philanthropy. Our findings reveal that the intensity of familism within FBs correlates positively with their philanthropic donations. This relationship suggests that FBs embed familial values deeply within their corporate strategies, thus enhancing their social reputation and contributing to societal welfare. Importantly, this study enriches the literature in the field of culture and corporate finance by illustrating how familism transcends internal business practices to influence community-oriented activities, broadening the understanding of how cultural values shape corporate philanthropy globally.

CSR awarding: A test of social reputation and impression management

CSR awarding: A test of social reputation and impression management

A Review on Deepfake generation and Detection based on Deep learning: Approaches, and Future Challenges

In recent years, applications of deepfake, particularly to achieve political, economic, or social reputation aims, have been become widespread. These applications do not require high-level professional technical skills. Also, deep learning techniques like Generative Adversarial networks (GANs) have enhanced deepfake, making it more realistic. So, several researchers are looking for developing an effective method to detect a fake image or video. This paper provides a comprehensive overview of several proposed deepfake generation approaches and the approaches used to detect any manipulation. Based on feature extraction methods, this study provides an extensive review of face manipulation, especially focusing on facial swap, re-enactment, and attribute manipulation. Additionally, the study describes all existing deepfake methods and evaluates the presented detection models based on the most effective deep learning algorithms by comparing their respective evaluation metrics. Moreover, it presents the challenges and gapes in trying to enhance and develop deepfake detection techniques. It assists readers in understanding the generation and detection of deepfake mechanisms and presents the field limitations and future works.

Impact of Motivation Factors for Using Generative AI Services on Continuous Use Intention: Mediating Trust and Acceptance Attitude

This study aims to empirically analyze the relationship between the motivational factors of generative AI users and the intention to continue using the service. Accordingly, the motives of users who use generative AI services are defined as individual, social, and technical motivation factors. This research verified the effect of these factors on intention to continue using the services and tested the meditating effect of trust and acceptance attitude. We tested this through verifying trust and acceptance attitudes. An online survey was conducted on language-based generative AI service users such as OpenAI’s ChatGPT, Google Bard, Microsoft Bing, and Meta-Lama, and a structural equation analysis was conducted through a total of 356 surveys. As a result of the analysis, individual, social, and technical motivational factors all had a positive (+) effect on trust and acceptance attitude on the attitude toward accepting generative AI services. Among them, individual motivation such as self-efficacy, innovation orientation, and playful desire were found to have the greatest influence on the formation of the acceptance attitude. In addition, social factors were identified as the factors that have the greatest influence on trust in the use of generative AI services. When it comes to using generative AI, it was confirmed that social reputation or awareness directly affects the trust in usability.

Emergence of economic and social disparities through competitive gift-giving

Several tiers of social organization with varying economic and social disparities have been observed. However, a quantitative characterization of the types and the causal mechanisms for the transitions have hardly been explained. While anthropologists have emphasized that gift exchange, rather than market exchange, prevails in traditional societies and shapes social relations, few mathematical studies have explored its consequences for social organizations. In this study, we present a simple model of competitive gift-giving that describes how gifts bring goods to the recipient and honor to the donor, and simulate social change. Numerical simulations and an analysis of the corresponding mean-field theory demonstrate the transitions between the following four phases with different distribution shapes of wealth and social reputation: the band, without economic or social disparities; the tribe, with economic but without social disparities; the chiefdom, with both; and the kingdom, with economic disparity and weak social disparity except for an outlier, namely, the “monarch”. The emergence of strong disparities is characterized by power law distributions and is attributed to the “rich get richer” process. In contrast, the absence of such a process leads to exponential distributions due to random fluctuations. The phases depend on the parameters characterizing the frequency and scale of gift interactions. Our findings provide quantitative criteria for classifying social organizations based on economic and social disparities, consistent with both anthropological theory and empirical observations. Thus, we propose empirically measurable explanatory variables and characteristic indices for the evolution of social organizations. The constructive model, guided by social scientific theory, can provide the basic mechanistic explanation of social evolution and integrate theories of the social sciences.

A Paradox of Inclusion in Cities: Homemaking of Refugees and Natives in Urban Space

AbstractIn this research, we aimed to explore a paradox of inclusion that emerges in the homemaking activities of refugees and natives. On one side, natives perceive threat from refugees and tend to preserve their home by excluding them from urban spaces. On the other side, refugees strive to make a new home and seek to regain homely feelings. Therefore, we examined inclusion both as an outcome variable (willingness for inclusion in the native context) and as a predictor (perceived inclusion in the refugee context). One distinctive aspect of this study is that both natives and refugees have comparably similar ethnocultural backgrounds (either Kurds or Arabs) re-encountered after the Syrian proxy war. A total of 421 natives (Study 1, Mage = 37.1, SD = 14.3; 50% women), and 889 refugees (Study 2, Mage = 36.59; SD = 12.51; 62% women) living in Mardin (a city in Turkey bordering Syria) were surveyed. Both groups of participants were asked about their daily life activities across places (dwelling, neighborhood, city center) as related to homemaking. In addition, natives were asked about the extent they perceived threat from, and ascribed social reputation to refugees, whereas refugees were asked about the extent they were satisfied with the urban life and spaces they felt homely. Our results highlight the importance of focusing on the inclusion paradox in the context of mobility (including forced migration), which generates a new disadvantaged layer in society even among people with similar ethnocultural backgrounds when one group is regarded as refugees and the other natives.

Effect of stakeholder pressure on environmental performance: Do virtual CSR, green credit, environmental and social reputation matter?

In today's globalized and interconnected world, businesses operate within an interconnected network with various stakeholders. Among these stakeholders, the pressure from consumers, investors, regulators, and communities for corporations to adopt environmentally responsible practices has intensified significantly. Therefore, this study investigates the relationship between stakeholder pressure and environmental performance (EP) in Pakistan's manufacturing sector while examining the potential moderating roles of environmental reputation, social reputation, virtual CSR, and green credit. Data was gathered by convenience sampling and a cross-sectional research approach. A structural questionnaire was given to 376 employees of manufacturing firms that are listed on the Pakistan Stock Exchange (PSE). The research hypotheses were tested using PLS-SEM techniques, which examined the interactions between these variables. The results reveal that stakeholder pressure significantly influences the environmental performance of manufacturing firms. Environmental reputation and virtual CSR negatively moderate the relationship between stakeholder pressure and EP. Moreover, social reputation and green credit positively moderate the relationship between stakeholder pressure and EP. These results contribute to the existing literature by shedding light on how stakeholder pressure influences firms' environmental behavior. Practical implications include integrating virtual CSR strategies, securing green financing options, and building strong environmental and social reputations to effectively respond to stakeholder demands and improve environmental performance.

Callousness, exploitativeness, and tracking of cooperation incentives in the human default network

Human cognitive capacities that enable flexible cooperation may have evolved in parallel with the expansion of frontoparietal cortical networks, particularly the default network. Conversely, human antisocial behavior and trait antagonism are broadly associated with reduced activity, impaired connectivity, and altered structure of the default network. Yet, behaviors like interpersonal manipulation and exploitation may require intact or even superior social cognition. Using a reinforcement learning model of decision-making on a modified trust game, we examined how individuals adjusted their cooperation rate based on a counterpart's cooperation and social reputation. We observed that learning signals in the default network updated the predicted utility of cooperation or defection and scaled with reciprocal cooperation. These signals were weaker in callous (vs. compassionate) individuals but stronger in those who were more exploitative (vs. honest and humble). Further, they accounted for associations between exploitativeness, callousness, and reciprocal cooperation. Separately, behavioral sensitivity to prior reputation was reduced in callous but not exploitative individuals and selectively scaled with responses of the medial temporal subsystem of the default network. Overall, callousness was characterized by blunted behavioral and default network sensitivity to cooperation incentives. Exploitativeness predicted heightened sensitivity to others' cooperation but not social reputation. We speculate that both compassion and exploitativeness may reflect cognitive adaptations to social living, enabled by expansion of the default network in anthropogenesis.

The Effect of Occupational Esteem of Skincare Employees on Customer Orientation

The purpose of this study is to find the effect of skincare employees’ occupational esteem on customer orientation. As a survey method, a questionnaire survey was conducted with skincare employees working in skincare facilities nationwide from September 15, 2023, to October 23, 2023, A total of 234 responses were analyzed. The analysis results are as follows: Firstly, it was observed that as skincare employees’ positive values and social reputation increased, their ability to understand customer needs also increased. Secondly, as skincare employees’ social reputation and positive values increased, their ability to satisfy customer needs also increased. Thirdly, it was found that as skincare employees’ positive values, relative benefits, and social reputation increased, their desire to provide services also increased. This indicates that if the employees feel a sense of achievement through their work and receive social recognition, they strive to understand customers’ needs and provide desired services. Therefore, it is considered that in order to enhance more competitive skincare facilities, it is necessary to come up with a program for providing and improving occupational esteem for skincare employees.

Incorporating reputation into reinforcement learning can promote cooperation on hypergraphs

Most previous studies that investigate the evolutionary dynamics of cooperation in the multi-agent social dilemma only consider the pairwise interactions. However, pairwise interactions alone cannot adequately characterize the higher-order interaction relationships that are commonly present in real-world social systems. In this study, we propose a spatial public goods game on uniform random hypergraphs with reputation mechanism and self-regarding Q-learning algorithm, where agents take an action based on the Q-value and update their reputation by the first-order evaluation norm. We define the reward as a function of the agent’s payoff and their reputation influenced by the social attention degree. Simulation results indicate that an increase in the social attention degree can effectively facilitate cooperation, as social reputation incentivizes agents to adopt cooperative actions to obtain optimal rewards. Additionally, we find that the discount factor and the learning rate both significantly impact the evolution of cooperation, with the influence of the learning rate on the evolution of cooperation being dependent on the discount factor. Finally, the robustness of this model is demonstrated based on the results of theoretical analysis.

Relational aggression in the context of personality metatraits

People that hurt others through relationally aggressive behavior (e.g., damaging social reputation, threats of rejection) have a particular personality profile. It consists of high neuroticism, and low extraversion, agreeableness, conscientiousness, and openness. Similar profiles of traits constitute broader personality dimensions known as metatraits of personality conceptualized in the Circumplex of Personality Metatraits (CPM). In the present study, we examined the location of proactive and reactive relational aggression in the circumplex of personality metatraits according to the CPM. Three hundred ninety participants (228 women and 162 men) responded to an online survey. Relational aggression was located between Gamma-Minus (Disharmony) and Alpha-Minus (Disinhibition), but fitted significantly to the theoretical location on the Gamma-Minus metatrait despite of the function (proactive vs. reactive) of relational aggression. We interpret the location of relational aggression within the circumplex of the metatraits with regards to temperamental correlates of metatraits (e.g., temperamental sensitivity).

How should regulatory schemes be optimized to enhance deterrence against medical insurance fraud by enrollees? Evidence from a discrete choice experiment in China

Medical insurance fraud (MIF) poses a substantial global financial challenge, necessitating effective regulatory strategies, especially in China, where such measures are in a critical developmental phase. This study investigates the effectiveness of various regulatory components in deterring MIF among enrollees and explores preference heterogeneity among individuals with different characteristics, utilizing a discrete choice experiment survey. Grounded in deterrence theory, our conceptual framework incorporates five attributes: intensity of economic penalties, restrictions on medical insurance benefits, deterioration of social reputation, and certainty and celerity of penalties. Employing a D-efficiency design, 24 choice sets were generated and blocked into three versions. A multistage stratified sampling method was adopted to collect data from the basic medical insurance enrollees in Shanghai. The survey was conducted from September to October 2022. The sample representativeness was further improved via the entropy balancing approach. Data from the final sample of 1034 respondents were analyzed using mixed logit models (MIXLs), incorporating interactions with individual characteristics to assess preference heterogeneity. Results reveal that escalating economic penalties, suspending insurance benefits, listing individuals as unfaithful parties, ensuring penalty certainty, and expediting enforcement significantly enhance the deterrent effect. We observed preference heterogeneity across different demographics, including age, gender, education, health status, and employment status. The study underscores the pivotal role of economic penalties in deterring MIF, while also acknowledging the significance of non-economic measures such as enforcement efficiency and social sanctions. These findings offer valuable insights for policymakers to tailor and strengthen regulatory schemes against MIF, contributing to the advancement of more effective and precise healthcare policies.

Anti-corruption reporting: a review empirical literature

Purpose This review summarizes the empirical literature dealing with anti-corruption disclosure as this specific type of disclosure has attracted a great deal of attention in accounting literature. Design/methodology/approach Keywords used to collect relevant papers from numerous electronic databases (e.g. Science Direct, Emerald, Wiley-Blackwell, Springer and Taylor and Francis) include “anti-corruption reporting” “anti-corruption disclosure”. The final sample encompasses a set of 35 empirical studies published between 2015 and the beginning of 2024. Findings The summary of reviewed studies suggests that anti-corruption empirical studies are mainly cross-country investigations. Two streams of research are identified: (i) the determinants of anti-corruption disclosure and (ii) the economic consequences of anti-corruption reporting. With respect to the first stream of research, six main categories of determinants are identified (corporate characteristics, corporate governance attributes, informal institutions, stakeholders’ pressures, country institutional effect and regulation effect). With respect to the second stream of research, findings show that anti-corruption reporting is negatively associated with profitability, reduces earnings management and enhances corporate social reputation. Practical implications With respect to regulators, this review sheds light on the importance of anti-corruption disclosure in the fight against corruption. It also suggests that the adoption of some regulations like the Directive 2014/95/EU in the European Union or the 2010 UK Bribery Act have contributed to more transparency. With respect to investors, the existence of some determinants of anti-corruption reporting (e.g. United Nations Global Compact membership, cross-listing, multinationality, board independence) may signal the adequacy of corporate reporting policy and that management is following an adequate strategy to fight corruption and enhance transparency. Originality/value This review offers future research avenues for accounting scholars with respect anti-corruption disclosure literature.

A mechanistic model of gossip, reputations, and cooperation

Social reputations facilitate cooperation: those who help others gain a good reputation, making them more likely to receive help themselves. But when people hold private views of one another, this cycle of indirect reciprocity breaks down, as disagreements lead to the perception of unjustified behavior that ultimately undermines cooperation. Theoretical studies often assume population-wide agreement about reputations, invoking rapid gossip as an endogenous mechanism for reaching consensus. However, the theory of indirect reciprocity lacks a mechanistic description of how gossip actually generates consensus. Here, we develop a mechanistic model of gossip-based indirect reciprocity that incorporates two alternative forms of gossip: exchanging information with randomly selected peers or consulting a single gossip source. We show that these two forms of gossip are mathematically equivalent under an appropriate transformation of parameters. We derive an analytical expression for the minimum amount of gossip required to reach sufficient consensus and stabilize cooperation. We analyze how the amount of gossip necessary for cooperation depends on the benefits and costs of cooperation, the assessment rule (social norm), and errors in reputation assessment, strategy execution, and gossip transmission. Finally, we show that biased gossip can either facilitate or hinder cooperation, depending on the direction and magnitude of the bias. Our results contribute to the growing literature on cooperation facilitated by communication, and they highlight the need to study strategic interactions coupled with the spread of social information.

A Study on the Influence of Consumer Perception, Norm and Face Consciousness on the Willingness to Purchase Counterfeit Luxury Goods: A Case of Generation Z

This study investigates the intricate dynamics shaping Generation Z's propensity to purchase counterfeit luxury goods in China, focusing on the influences of consumer perception, social norms, and face consciousness. Counterfeit luxury consumption presents a compelling context for understanding consumer behavior, particularly among younger demographics in emerging markets like China. Drawing upon a mixed-methods approach, including surveys and interviews, this research delves into the nuanced interplay between various psychological and sociocultural factors driving the consumption of counterfeit luxury goods. The theoretical framework integrates concepts from consumer behavior, social psychology, and cultural studies to elucidate the mechanisms underlying Generation Z's decision-making processes regarding counterfeit luxury purchases. Specifically, consumer perception is examined in terms of the perceived value, quality, and social status associated with counterfeit luxury goods. Additionally, social norms are analyzed to understand how peer influence, societal expectations, and cultural norms shape individuals' attitudes and behaviors towards counterfeit consumption. Furthermore, the notion of face consciousness, rooted in Chinese culture's emphasis on social reputation and saving face, is explored as a crucial determinant of counterfeit purchasing behavior among Generation Z consumers. Through empirical data analysis, this study uncovers multifaceted insights into the willingness of Generation Z in China to engage in counterfeit luxury consumption. Findings reveal that while perceptions of value and social status play significant roles in driving counterfeit purchases, social norms exert a compelling influence, often outweighing individual perceptions. Moreover, face consciousness emerges as a potent motivator, as individuals navigate the delicate balance between maintaining social esteem and satisfying personal desires. The implications of this research extend to both academia and industry, offering valuable insights for marketers, policymakers, and brand managers seeking to address the challenges posed by counterfeit luxury consumption among Generation Z consumers in China. By understanding the complex interplay of consumer perception, social norms, and face consciousness, stakeholders can develop targeted interventions and strategies aimed at mitigating the demand for counterfeit goods while fostering authentic brand loyalty and consumer trust.

Image splicing detection using low-dimensional feature vector of texture features and Haralick features based on Gray Level Co-occurrence Matrix

Digital image forgery has become hugely widespread, as numerous easy-to-use, low-cost image manipulation tools have become widely available to the common masses. Such forged images can be used with various malicious intentions, such as to harm the social reputation of renowned personalities, to perform identity fraud resulting in financial disasters, and many more illegitimate activities. Image splicing is a form of image forgery where an adversary intelligently combines portions from multiple source images to generate a natural-looking artificial image. Detection of image splicing attacks poses an open challenge in the forensic domain, and in recent literature, several significant research findings on image splicing detection have been described. However, the number of features documented in such works is significantly huge. Our aim in this work is to address the issue of feature set optimization while modeling image splicing detection as a classification problem and preserving the forgery detection efficiency reported in the state-of-the-art. This paper proposes an image-splicing detection scheme based on textural features and Haralick features computed from the input image’s Gray Level Co-occurrence Matrix (GLCM) and also localizes the spliced regions in a detected spliced image. We have explored the well-known Columbia Image Splicing Detection Evaluation Dataset and the DSO-1 dataset, which is more challenging because of its constituent post-processed color images. Experimental results prove that our proposed model obtains 95% accuracy in image splicing detection with an AUC score of 0.99, with an optimized feature set of dimensionality of 15 only.