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Creativity plays a crucial role in interpersonal conflict within organizations, yet little research has explored its antecedents in this context. This study aims to investigate power and gender as the main determinants of creativity in interpersonal conflict within organizational contexts.

Two studies were conducted. The first study involved 226 employees from various organizations (M_age = 39.39, SD = 10.39), whereas the second study used a conflict simulation with 160 participants (M_age = 36.90, SD = 10.45) forming dyads. Both studies investigated the impact of relative power (i.e. having more power than the other person) on creativity in conflict, with a focus on the moderating role of gender. Study 2 also manipulated contextual creativity, which served as an additional moderator in this relationship.

Results largely supported our hypotheses, indicating a positive relationship between relative power and creativity in conflict. Importantly, this relationship was stronger among women. Study 2 further focused on the distinct dimensions of creativity, highlighting differences between idea originality and effectiveness.

The findings hold practical significance for organizational leaders and conflict resolution practitioners, and they further underscore the importance of considering gender dynamics in conflict resolution processes within organizations.

This research contributes novel insights into the understanding of creativity within organizational conflicts, emphasizing the interplay between relative power, gender and creativity. Additionally, the exploration of different dimensions of creativity (i.e. originality and effectiveness) adds depth to existing literature in this area.

Internet search is a $120bn business that answers lists of search terms or keywords with relevant links to Internet webpages. Only a few companies have sufficient scale to compete and thus economics of the process are paramount. This study aims to develop a detailed industry-specific modeling of the economics of internet search.

The current research develops a stochastic model of the process of Internet indexing, search and retrieval in order to predict expected costs and revenues of particular configurations and usages.

The models define behavior and economics of parameters that are not directly observable, where it is difficult to empirically determine the distributions and economics.

The model may be used to guide the economics of large search engine operations, including the advertising platforms that depend on them and largely fund them.

The paper analyses the role and impact of Twitter in Spanish political communication, specifically focusing on how the platform influences the visibility and relevance of political candidates' messages during various election campaigns and across various political dimensions – government vs. opposition, traditional vs. populist, and left-wing vs. right-wing.

The study employs a longitudinal quantitative analysis perspective regarding the number and impact of messages posted by the main candidates in the six general elections held in Spain during the period 2011–2023. The tweets were automatically collected through the Twitter API for 54 days, between the call for elections and voting. The equalisation versus normalisation framework regarding social media is used.

The findings showed that the dissemination of messages on Twitter exhibited a skewed distribution characterized by notable differences in the unequal relevance of tweets between candidates on the left-right and traditional-populist axes, especially among the tweets with the highest impact. Tweets from opposition and populist candidates presented a greater level of relevance than those from government and conventional candidates. No discernible differences were identified along the ideological spectrum, indicating a high degree of cross-partisan engagement on social media. Findings indicate that opposition and populist candidates are strategically utilising Twitter, challenging the notion of normalisation and emphasising the equalising potential of social networks.

The study offers a long-term view of political communication, which is relatively rare in social media research, often focused on shorter time frames or single election cycles. The multidimensional comparison provides nuanced insights into how different political identities and affiliations are manifested and engaged with on social media.

Due to the rapid growth of digital economy, improving employees’ creativity is becoming essential to optimizing the development of organizations. This study investigates how enterprise social media can enhance employee creativity and develops an integrated model based on communication visibility and social capital theories.

A two-stage questionnaire was conducted on full-time employees with enterprise social media experience. The first round of this study distributed 1,048 questionnaires and collected 639 valid sample data. A month later, the second survey was sent to the first valid respondents, with 421 valid sample data collected within a week.

Results show that visibility has a positive influence on employee creativity, in which expertise recognition and network recognition play a mediating role. The findings also indicate that bridging social capital positively moderates the effect of visibility on expertise recognition, and bonding social capital positively moderates the effect of visibility on network recognition.

This study contributes to a better understanding of the benefits of enterprise social media by uncovering the mechanism and theoretical boundary of the effect of visibility on employee creativity.

In the 21st century, libraries are experiencing a significant decline in users due to shifting reading habits and the impact of technology, necessitating library transformation and a heightened emphasis on library marketing. Special libraries, in particular, rely heavily on patrons for survival, often due to their private ownership and limited resources. This paper aims to discuss the aforementioned objectives.

This paper examines the Taste Library, a special library in Hong Kong, and analyzes its current practices based on an interview with its founder, website content, and social media presence. The 7Ps Marketing Mix model is employed to assess the strengths and weaknesses of the library's current market position.

The Taste Library's existing practices exhibit limitations in attracting young patrons. To address this issue, we propose marketing strategies focused on enhancing social network presence, offering digitized content, and engaging in school outreach.

By concentrating on youth marketing, this study offers valuable insights for special libraries in developing strategic plans for transitioning and maintaining sustainability.

Few studies concentrate on marketing small special libraries, particularly in the East, within today's digitized economy.

In the era of artificial intelligence, natural language processing (NLP) models are revolutionizing numerous sectors. This research aims to explore the perceived value of them among university students. In particular, it aims to investigate how gender may influence students’ intention to use these models in educational contexts, highlighting potentially significant differences that could inform the implementation and adoption of educational technologies.

This study investigates the relationship between perceived value and students' intention to adopt NLP models, considering gender as a moderator. The research involves 562 students from the University of Salerno, in Italy, and uses confirmatory factor analysis to evaluate the reliability and validity of the measurement scales. A regression model with robust errors is used to explore the moderating role of gender on the relationship between perceived value and intentions of use of NLP models.

The results reveal a significant positive association between perceived value and intention to use NLP models, confirming that students with higher perceived value are more likely to adopt these technologies. Furthermore, gender moderates this relationship, indicating that females are less prone to use NLP models than male counterparts.

Research takes on a significant role in the academic field, underlining the importance of adapting teaching practices to the increasingly widespread digitalization. The inclusion of NLP models in university programs emerges as a possible improvement of the learning experience, ensuring cutting-edge education in tune with the needs of the digital society.

The purpose of this paper is to report on a study into the lived experience of skin picking disorder (SPD) and to explore the psychological impact of the disorder.

Researchers employed a qualitative phenomenological approach, using Interpretative phenomenological analysis (IPA).

The study found that the sense of shame, common among participants, led to self-stigma, hiding, concealing and avoidance. Shame of SPD also interplayed with and compounded the shame of loneliness.

Limitations included a lack of a longitudinal component to the work. This is deemed important, as both the SPD and the experience of loneliness can shift over time as circumstances change, and individuals develop strategies for coping or, conversely, experience a worsening of the condition and the shame, loneliness and social isolation it can induce.

This study draws attention to the complex nature of both SPD and loneliness. It highlights how those living with the disorder are reluctant to seek help, resulting in low rates of treatment access and distrust in health providers.

As SPD is little understood, it can be erroneously deemed a “choice”; seldom discussed as a condition, it can lead to exclusion and withdrawal. The sense of shame of both appearance and behaviour interplays with and compounds the shame of loneliness and can force the individual into a further cycle of withdrawal and isolation.

While research suggests SPD should be acknowledged as a public health issue, research and literature is still relatively limited, and there are no qualitative phenomenological studies as yet that report on the lived experience of SPD.

A significant autism employment gap persists in many countries. This study examines how the traditional job interview might form an obstacle to autistic people obtaining employment. It then offers a range of strategies that could make the traditional job interview more effective in allowing employers to identify and hire those who have an “autism advantage”.

A triangulated, qualitative approach is employed, comprising (1) five focus groups with a total of 23 students at a UK university who identity as autistic, and (2) semi-structured interviews with five of their support practitioners. Thematic analysis was then applied to identify causes, effects and possible solutions.

The analysis identified three main strategies, and two sub-strategies, for refining the traditional job interview by reducing or removing the obstacle it presents to autistic people seeking employment. These comprise abandoning the traditional interview, adapting it (divided in to adjusting and augmenting sub-strategies) and accepting it.

Three conclusions were developed from the analysis: first, that while the traditional interview tends to be biased against autistic people, it is not in itself a particularly acute method for selecting job candidates; second, that the application of universal design would be beneficial not only to neurodivergent people, but also to neurotypicals and employers; third, that the fear of disclosure represents a major obstacle to autistic people trusting in schemes intended to assist them.

Firms in emerging economies are generally at a disadvantage in terms of resources, which may limit their digital transformation. The Chinese government has designed and promulgated a series of wind power policies from the perspectives of support and regulation. The former provides scarce resources for enterprises and thus alleviating financial constraints. While the latter increases the demands for advanced technologies, thereby triggering resource bricolages. This study aims to clarify the impact of industrial policy on the digital transformation of the Chinese wind power industry, and the role of financing constraint and resource bricolage in the above relationship.

Based on the data of listed companies in the Chinese wind power industry from 2006 to 2021, this study clarifies the impact and mechanism of industrial policy on firm digital transformation with fixed effect regression models.

Empirical results indicate that both supportive and regulatory policies are the cornerstone of the digital transformation of the Chinese wind power industry. Financial constraint and resource bricolage, respectively, mediate the impact of supportive and regulatory policies. However, the mix of supportive and regulatory policies inhibits digital transformation. Moreover, industrial policies are more effective for the digital transformation of state-owned enterprises, as well as enterprises in economically underdeveloped regions.

This study investigates the path of government intervention driving firm digital transformation from the resource-related perspective (i.e. financial constraint and resource bricolage), and its analytical framework can be extended based on other theories. The combined effects of cross-sectoral policies (e.g. wind power policy and digital infrastructure policy) can be further assessed. The marginal net benefit of government intervention can be calculated to determine whether it is worthwhile.

This study emphasizes the necessity of government intervention in the digital transformation of enterprises in emerging economies. The governments should align the policy targets, clarify policy recipients and modify policy process of different categories of industrial policies to optimize the effectiveness of policy mix. Given that the effectiveness of government intervention varies among different categories of enterprises, the competent agencies should design and promulgate differentiated industrial policies based on the heterogeneity of firms to improve the effectiveness and efficiency of industrial policies.

This is one of the earliest explorations of industrial policies’ effect on the digital transformation of the renewable energy sector in emerging economies, providing new evidence for institutional theory. Meanwhile, this study introduces financial constraint and resource bricolage into the research framework and attempts to uncover the mechanism of industrial policy driving the digital transformation of enterprises in emerging economies. Besides, to expand the understanding of the complex industrial policy system, this study assesses the effectiveness of the industrial policy mix.

In the process of building the “Belt and Road” and “Bright Road” community of interests between China and Kazakhstan, this paper proposes the construction of an inland nuclear power plant in Kazakhstan. Considering the uncertainty of investment in nuclear power generation, the authors propose the MGT (Monte-Carlo and Gaussian Radial Basis with Tensor factorization) utility evaluation model to evaluate the risk of investment in nuclear power in Kazakhstan and provide a relevant reference for decision making on inland nuclear investment in Kazakhstan.

Based on real options portfolio combined with a weighted utility function, this study takes into account the uncertainties associated with nuclear power investments through a minimum variance Monte Carlo approach, proposes a noise-enhancing process combined with geometric Brownian motion in solving complex conditions, and incorporates a measure of investment flexibility and strategic value in the investment, and then uses a deep noise reduction encoder to learn the initial values for potential features of cost and investment effectiveness. A Gaussian radial basis function used to construct a weighted utility function for each uncertainty, generate a minimization of the objective function for the tensor decomposition, and then optimize the objective loss function for the tensor decomposition, find the corresponding weights, and perform noise reduction to generalize the nonlinear problem to evaluate the effectiveness of nuclear power investment. Finally, the two dimensions of cost and risk (estimation of investment value and measurement of investment risk) are applied and simulated through actual data in Kazakhstan.

The authors assess the core indicators of Kazakhstan's nuclear power plants throughout their construction and operating cycles, based on data relating to a cluster of nuclear power plants of 10 different technologies. The authors compared it with several popular methods for evaluating the benefits of nuclear power generation and conducted subsequent sensitivity analyses of key indicators. Experimental results on the dataset show that the MGT method outperforms the other four methods and that changes in nuclear investment returns are more sensitive to changes in costs while operating cash flows from nuclear power are certainly an effective way to drive investment reform in inland nuclear power generation in Kazakhstan at current levels of investment costs.

Future research could consider exploring other excellent methods to improve the accuracy of the investment prediction further using sparseness and noise interference. Also consider collecting some expert advice and providing more appropriate specific suggestions, which will facilitate the application in practice.

The Novel Coronavirus epidemic has plunged the global economy into a deep recession, the tension between China and the US has made the energy cooperation road unusually tortuous, Kazakhstan in Central Asia has natural geographical and resource advantages, so China–Kazakhstan energy cooperation as a new era of opportunity, providing a strong guarantee for China's political and economic stability. The basic idea of building large-scale nuclear power plants in Balkhash and Aktau is put forward, considering the development strategy of building Kazakhstan into a regional international energy base. This work will be a good inspiration for the investment of nuclear generation.

This study solves the problem of increasing noise by combining Monte Carlo simulation with geometric Brownian motion under complex conditions, adds the measure of investment flexibility and strategic value, constructs the utility function of noise reduction weight based on Gaussian radial basis function and extends the nonlinear problem to the evaluation of nuclear power investment benefit.