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During the COVID-19 pandemic, an infodemic erupted on social media, leading to a surge in negative disclosure behaviors such as expressing dissatisfaction and releasing negative emotions. By extending the elaboration likelihood model and the Big Five personality theory to the domain of online self-disclosure, we aimed to identify the factors that influence negative disclosure behavior.

We investigated how the features of negative information content, information sources and recipients’ social perceptions influence how social media users disclose negative information. We also examined the moderating roles of personality traits in this process. To validate the model and test our hypotheses, we collected cross-sectional data from 456 social media users.

Empirical results reveal that (1) information overload, topic relevance, attractiveness of information sources, peer approval of negative disclosure and social influence on negative information strengthen the intention to disclose negative information. (2) The perception of social risk weakens the intention to disclose negative information. (3) Openness to experience, extraversion and neuroticism strengthen the relationship between the intention to disclose negative information and actual disclosure behavior.

Our results not only provide new perspectives on the decision-making mechanisms behind negative disclosure behavior but also extend personality research within the context of the dissemination of negative information. Furthermore, it offers insights into negative information dissemination on social media platforms, with significant implications for various stakeholders.

Online open innovation platforms provide opportunities for product users to participate in the innovation process and contribute their ideas to the platform. Nonetheless, they also present a significant challenge for platform managers, who select high-quality innovations from a massive collection of information with diverse quality.

In this study, the authors employed a machine learning method to automatically collect a real dataset of 2,276 innovations and 30,004 detailed comments from the online platform of IdeaExchange and then conducted empirical experiments to verify the study hypothesis.

Results show that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness and openness to experience and the quality of an innovation.

Results showed that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness, openness to experience and the quality of innovations.

This study combined the Big Five personality traits theory and social network theory to examine the association between user intrinsic personality traits, social network position and the quality of their innovative ideas in the context of online innovation platforms. Additionally, the findings provide new insights for platform managers on how to select high-quality innovation information by considering user personality traits and their social network position.

Large volumes of users' creative information have rapidly become vital resources in the open innovation platforms, so it is crucial to identify high-quality information from massive creative information. However, the existing literature on the quality of creative information only focuses on the information characteristics or publishers' features.

In this paper, the authors used the elaboration likelihood model to examine the joint effect of central route factors (information characteristics: timeliness, readability and sentiment) and peripheral route factors (source characteristics: personality traits, past successful experiences and social network location) on the quality of creative information. Furthermore, the author explored the moderating roles of companies' support between central and peripheral route factors on the quality of creative information. Finally, binary logistic regression was adopted to test the research hypotheses on the empirical data from Salesforce.

The results indicated that users with high extroversion, conscientiousness, social centrality and prior success rate tended to propose high-quality information. Meanwhile, information timeliness, readability and sentiment also negatively influence the quality of creative information.

Different from previous studies, the study findings not only provide insights on identifying the quality of creative information from an information perspective, but also promotes the awareness of the intrinsic personality traits of information users and innovative support efforts by platforms and their managers.

The present study aims to propose a novel employee-centric framework for the study of employer brand attractiveness. This framework disentangles the role of employer attributes, employee benefits and employee perceived value in the study of employer brands to better develop policies for talent attraction. Additionally, this study formulates a research agenda to help advance an employee-centric view of the employer’s brand management by following the tradition of customer-centric research and identifying benefits and forms of value that are yet unexplored.

This paper performs a systematic and critical review of the literature on employer brand attraction from the lens of means-end chains to examine how the notions of “employer attributes”, “employee benefits” and “perceived value” have been addressed in past studies and what relations have been established among these three concepts.

The results unveil the existing conflation among attributes, benefits and value in the conceptualization of employer brand attractiveness. By proposing an employee-centric framework following the tenets of current consumer-centric paradigms, this paper disentangles the notions of attributes, benefits and value in the creation of attractive employer brands; establishes a hierarchical relationship among them; and suggests studying the multiple paths of relationships between attributes and benefits. These conditions should help organizations understand how to create successful strategies to ultimately ensure that they are selected as employers of choice.

Further research is needed to clarify the domains in which the already studied empirical relations hold. This could be achieved by conducting a laddering process based on a means–end chain approach. Additionally, the impact of this framework on the construction of effective value propositions and employee market segmentation should be further explored.

This paper has revisited the construct of employer brand from a means–ends approach to propose an employee-centric view guiding employer brand strategies. As competition for the best employees sharpens, understanding how employer brand traits are valued in the eyes of different segments of employees and applicants may help organizations to develop more effective strategies to attract the best talent.

The main objective of the study was to understand the impact of intrinsic and extrinsic motivation on productivity at two different times: before and after the pandemic era, among middle and top-level professionals across India.

The study has adopted a shortitudinal approach. The data were collected two times from the same respondents: before the pandemic (T1) and after the pandemic (T2) following the convenience sampling. At T1, we received responses from 321 respondents. At T2, we received only 203 responses while contacting the same respondents. Thus, the final sample size was only 203 respondents.

The results revealed that in the pre-pandemic times, both intrinsic and extrinsic motivation impacted the productivity of middle and top-level professionals. Further, the study also revealed that only intrinsic motivation impacted productivity after the pandemic. Moreover, generational cohorts (pre-liberalization, early-liberalization and rapid growth generations) moderated the relationship between intrinsic and extrinsic motivation and Productivity at T1 and T2.

Woven in the multiple theories, the study has some practical and theoretical nuances. The intrinsic and extrinsic motivations were rooted in the self-determination theory. The results also supported that extrinsic reward cannot increase intrinsic motivation in difficult times, and only intrinsic motivation is constant at all times, contributing to productivity. The HR department should understand the importance of intrinsic motivation and design employee benefits and policies.

The results also supported that extrinsic reward cannot increase intrinsic motivation in difficult times, and only intrinsic motivation is constant at all times, contributing to productivity. The HR department should understand the importance of intrinsic motivation and design employee benefits and policies.

Following the generational view, the study added that different generation reacts differently to the turbulent times.

In data grids, replication has been regarded as a crucial optimization strategy. Computing tasks are performed on IoT gateways at the cloud edges to obtain a prompt response. So, investigating the data replication mechanisms in the IoT is necessary. Henceforth, a systematic survey of data replication strategies in IoT techniques is presented in this paper, and some suggestions are offered for the upcoming works. In two key classifications, various parameters dependent on the analysis of the prevalent approaches are considered. The pros and cons associated with chosen strategies have been explored, and the essential problems of them have been presented to boost the future of more effective data replication strategies. We have also discovered gaps in papers and provided solutions for them.

Progress in Information Technology (IT) growth has brought the Internet of Things (IoT) into life to take a vital role in our everyday lifestyles. Big IoT-generated data brings tremendous data processing challenges. One of the most challenging problems is data replication to improve fault-tolerance, reliability, and accessibility. In this way, if the primary data source fails, a replica can be swapped in immediately. There is a significant influence on the IoT created by data replication techniques, but no extensive and systematic research exists in this area. There is still no systematic and full way to address the relevant methods and evaluate them. Hence, in the present investigation, a literature review is indicated on the IoT-based data replication from papers published until 2021. Based on the given guidelines, chosen papers are reviewed. After establishing exclusion and inclusion criteria, an independent systematic search in Google Scholar, ACM, Scopus, Eric, Science Direct, Springer link, Emerald, Global ProQuest, and IEEE for relevant studies has been performed, and 21(6 paper analyzed in section 1 and 15 paper analyzed in section 3) papers have been analyzed.

The results showed that data replication mechanisms in the IoT algorithms outperform other algorithms regarding impressive network utilization, job implementation time, hit ratio, total replication number, and the portion of utilized storage in percentage. Although a few ideas have been suggested that fix different facets of IoT data management, we predict that there is still space for development and more study. Thus, in order to design innovative and more effective methods for future IoT-based structures, we explored open research directions in the domain of efficient data processing.

The present investigation encountered some drawbacks. First of all, only certain papers published in English were included. It is evident that some papers exist on data replication processes in the IoT written in other languages, but they were not included in our research. Next, the current report has only analyzed the mined based on data replication processes and IoT keyword discovery. The methods for data replication in the IoT would not be printed with keywords specified. In this review, the papers presented in national conferences and journals are neglected. In order to achieve the highest ability, this analysis contains papers from major global academic journals.

To appreciate the significance and accuracy of the data often produced by different entities, the article illustrates that data provenance is essential. The results contribute to providing strong suggestions for future IoT studies. To be able to view the data, administrators have to modify novel abilities. The current analysis will deal with the speed of publications and suggest the findings of research and experience as a future path for IoT data replication decision-makers.

In general, the rise in the knowledge degree of scientists, academics, and managers will enhance administrators' positive and consciously behavioral actions in handling IoT environments. We anticipate that the consequences of the present report could lead investigators to produce more efficient data replication methods in IoT regarding the data type and data volume.

This report provides a detailed literature review on data replication strategies relying on IoT. The lack of such papers increases the importance of this paper. Utilizing the responses to the study queries, data replication's primary purpose, current problems, study concepts, and processes in IoT are summarized exclusively. This approach will allow investigators to establish a more reliable IoT technique for data replication in the future. To the best of our understanding, our research is the first to provide a thorough overview and evaluation of the current solutions by categorizing them into static/dynamic replication and distributed replication subcategories. By outlining possible future study paths, we conclude the article.

This research aims to investigate the relationship between employer branding and its antecedent organisational culture within the context of the private banking sector. The study also investigates the relationship between employer branding and employee brand equity as a consequential construct. Additionally, the mediating role of trust and the moderating role of gender in the relationship between employer branding and employee brand equity has been examined.

The present study’s findings result from data analysis collected from a sample of 454 employees working in private banks in India. The data analysis was conducted utilising the structural equation modelling technique with the assistance of analysis of moment structures (AMOS) software.

The study’s findings indicate that supportive and bureaucratic (formal) culture in private banks exhibit a significant relationship with employer branding. However, the relationship between innovative culture and employer branding was found to be insignificant. The research also reveals a significant positive association between employer branding and employee brand equity variables: brand consistent behaviour, brand endorsement and brand allegiance. Further, the study highlights the mediating role of employee trust in management in the relationship between employer branding and employee brand equity. Examining demographic variables suggests that gender moderates the relationship between employer branding and employee brand equity.

The originality of this study lies in its exploration of the critical role of organisational culture variables in shaping employer branding within the context of private banks. The findings highlight that cultivating supportive and bureaucratic cultures can effectively enhance the employer branding of private banks. The study emphasises the outcomes of employer branding initiatives, signifying that they contribute to developing brand equity among employees. This leads to long-term employee commitment and advocacy towards the organisation, as employees become brand advocates for the bank with which they are affiliated. The study contributes to a better understanding of the relationship between organisational culture, employer branding and employee brand equity, providing valuable implications for the private banking sector aiming to reinforce their employer brand and increase employee engagement.

The purpose of this paper is to provide the historical background of genealogical records and analyze the value of Chinese genealogical research through the study of names and genealogical resources.

The paper examines the historical evolution and value of Chinese genealogical records, with the focus on researching the Islamic Chinese names used by the people living in Guilin. The highlight of this paper includes the analysis and evolution of the Islamic Chinese names commonly adopted by the local people in Guilin. It concludes with the recommendations on emphasizing and making the best use of genealogical records to enhance the research value of Chinese overseas studies.

The paper covers the history of Islam and describes how the religion was introduced into China, as well as Muslims' ethnicity and identity. It also places focus on the importance of building a research collection in Asian history and Chinese genealogy.

This research study has a strong subject focus on Chinese genealogy, Asian history, and Islamic Chinese surnames. It is a narrow field that few researchers have delved into.

The results of this study will assist students, researchers, and the general public in tracing the origin of their surnames and developing their interest in the social and historical value of Chinese local history and genealogies.

The study of Chinese surnames is, by itself, a particular field for researching the social and political implications of contemporary Chinese society during the time the family members lived.

Very little research has been done in the area of Chinese local history and genealogy. The paper would be of value to researchers such as historians, sociologists, ethnologists and archaeologists, as well as students and anyone interested in researching a surname origin, its history and evolution.

The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.

The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.

The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.

Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.

Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.

This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.