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This study aims to investigate how firms of different sizes respond to competitive pressure from peers.

This study employs machine learning techniques to measure competitive pressure based on management discussion and analysis (MD&A) documents and then utilises the constructed pressure indicator to explore the relationship between competitive pressure and corporate risk-taking behaviours amongst firms of different sizes.

We find that firm sizes are positively associated with their risk-taking behaviours when firms respond to competitive pressure. Large firms are inclined to exhibit a high level of risk-taking behaviours, whereas small firms tend to make conservative decisions. Regional growth potential and institutional ownership moderate the relationships.

Utilising text mining techniques, this study constructs a novel quantitative indicator to measure competitive pressure perceived by focal firms and demonstrates the heterogeneous behaviour of firms of different sizes in response to competitive pressure from peers, advancing research on competitive market pressures.

In theory, the impact of debt liquidity risk (DLR) on the firm's future growth is ambiguous. This study aims to examine the empirical relationship between the DLR and firms' growth rate using annual data for USA companies from 1976 to 2020.

Given the longitudinal nature of the data, the author uses OLS (ordinary least squares) regression methodology with fixed effects to control for unobserved characteristics that might affect the dependent variable. Instrument variable regression is also used to address the potential endogeneity problem.

The results show that firms having higher DLR, as proxied by more short-term debt, experience lower growth rate. An increase in firms' short-term debt decreases the firms' future growth rate as evidenced by lower assets, revenue and employee growth rate. Moreover, the authors' results show that small firms or firms with more investment opportunities grow fast if the firms take higher DLR. Finally, cyclical firms with higher DLR exhibit lower growth rate during the credit tighten period. The authors' results hold for both the pre-zero lower bound (ZLB) era and ZLB period.

To the authors' best knowledge, this is one of the earliest studies to carefully examine the effects of DLR on firms' growth rate. While prior research finds that firms with higher growth potential, measured by market-to-book (MTB) ratio, use more short-term debt, the authors' research directly addresses whether DLR affects firms' future growth rate. The authors’ findings also help explain why firms with high growth potential use more short-term debt.

The human resource and talent management fields have been increasingly focusing on the process and criteria to identify employees’ potential for career advancement due to their impact on the competitive advantage of organizations. This paper expands the extant theoretical and empirical evidence regarding these complex decisions, namely through the combined analysis of multidimensional sources of employees' capital.

This is a cross-sectional study. Data were collected from 384 individuals assessed by their line managers. The research model and hypotheses were tested using structured equation modeling.

The results show a positive and significant influence of four employees’ capital sources, namely: human capital (what you know), social capital (whom you know), psychological capital (who you are) and reputational capital (how others perceive us) with regard to judgments of potential for career advancement. The model explains 52% of the total variance in those judgments.

The data were collected using a questionnaire at a single point in time and thus, not allowing cause-effect inferences.

The results provide guidance to organizational leaders to improve the decision-making process regarding judgments of potential for career advancement.

To our knowledge, this is the first study to examine managers’ judgments regarding the potential for career advancement using four sources of employees' capital: human, social, psychological and reputational capital. Furthermore, it considers that reputation plays a mediation role.

:在人力資源和人才管理的領域裡,越來越多的焦點被放在找出員工職業發展所需潛能的過程和標準上,這是因為機構和組織的競爭優勢會受其影響。本文擬擴展關於這類複雜的判斷和決定的現存理論和經驗證據; 研究人員使用的方法為對員工資本的多維來源進行綜合分析。

:本研究為橫斷面研究。數據來自384 名受其直線經理評估的個別員工; 研究人員使用了結構方程模型,去測試其研究模型和各個假設。

:研究結果顯示,就員工職業發展所需潛能的判斷而言,存在著會帶來正面和重大影響的四個員工資本來源,這包括人力資本 (你所瞭解的事物) 、社會資本 (你所認識的人物) 、心理資本 (你是誰) 和聲譽資本 (我們予人的印象) 。有關的模型可說明判斷百分之五十二的總方差。

:由於數據的收集是於單一時間點和透過問卷調查而完成的,故未能收因果推斷的效果。

:研究結果給予組織領袖實際的指引,使他們在關於職業發展所需潛能的判斷上，能夠改善其決策的過程。

:就我們所知,本研究為首個研究、以四個員工資本來源,即人力資本、社會資本、心理資本和聲譽資本,去探討經理如何就職業發展所需的潛能作出判斷; 而且，本研究認為聲譽是扮演著調節角色的。

Tanzania is rich in small hydropower (SHP) potentials. However, many of these potentials have yet to be fully used, and more than two-thirds of its rural population lacks access to electricity. The purpose of this paper is to explore the role of SHP stations in improving rural welfare in the southern highlands of Tanzania. It further explores the history, cost-effective analysis and threats to the sustainability of SHP as one of the renewable energy sources.

The study uses a qualitative research design to explore respondents’ views on the role of SHP stations in facilitating rural electrification and welfare improvement. Primary data were gathered using semi-structured interviews with the 27 key informants and beneficiaries of SHP stations from the Southern Highlands of Tanzania. In addition, the study used documentary research to complement the information from the field survey.

The findings found that SHP stations enhance rural electrification and welfare by providing electricity in remote areas with sparse populations. They operate as standalone off-grids, often by church communities and individuals. However, the sustainability of SHP stations is hampered by challenges such as climate change impacts, high capital investment costs, heavy siltation of small reservoirs, skilled manpower shortages, limited local manufacturing capabilities and infrastructural issues.

The study contributes to the ongoing debate on renewable energy supply and uses, focusing on how SHP stations could contribute to sustainable rural electrification and achieve the 2030 United Nations agenda for sustainable development, which, among other things, aims to safeguard access to sustainable and modern energy and alleviate energy poverty.

Shadegan County as a wetland area was selected because of its susceptibility to flooding hazards and inundation. The purpose of this paper is to analyze flooding hazard based on the analytical hierarchy process methodology.

The eight influencing factors (slope, distance from wetland, distance from river, drainage density, elevation, curve number, population density and vegetation density) were considered for flood mapping within the Shadegan County using analytical hierarchical process, geographical information system and remote sensing. The validation of the map was conducted based on the comparison of the historical flood inundation of April 21, 2019.

The results showed that around 32.65% of the area was under high to very high hazard zones, whereas 44.60% accounted for moderate and 22.75% for very low to the low probability of flooding. The distance from Shadegan Wetland has been gained high value and most of the hazardous areas located around this wetland. Finally, the observed flood density in the different susceptibility zones for the very high, high, moderate, low and very low susceptible zones were 0.35, 0.22, 0.15, 0.19, and 0.14, respectively.

To the best of the authors’ knowledge, the flood susceptibility map developed here is one of the first studies in a built wetland area which is affected by anthropogenic factors. The flood zonation map along with management and restoration of wetland can be best approaches to reduce the impacts of floods.

Technostress reduces employees' work performance and increases their turnover intentions, such that technostress harms organizations' success. This paper investigates how the digital mindset of employees, reflecting their cognitive filter while using digital technologies, influences reactions to techno-stressors.

In this quantitative study, the authors conducted a survey among 151 employees who regularly use digital technologies and encounter various techno-stressors in their daily work. To build this research model and evaluate the influence of employees’ digital mindset on technostress, the authors followed arguments from the transactional model of stress. The authors evaluated our research model using the covariance-based structural equation model.

The study findings reveal that employees’ digital mindset influences technostress. Employees with high levels of digital mindset react with less adverse effects on perceived techno-stressors. Further, the authors find that employees with high levels of digital mindset perform well and are satisfied with their job. The authors contribute to technostress research by revealing that digital mindset buffers the adverse effects of techno-stressors. The authors also contribute to research on digital mindset by showing that it influences psychological and behavioral reactions to techno-stressors.

This study develops and empirically tests an integrated model of technostress to explain how digital mindset mitigates technostress. The study findings outline relevant research avenues for studies investigating employees’ characteristics and technostress.

The purpose of this research is to determine key behaviours to be efficient in identifying and developing employees' talent. The article aims to address the relationship between learning agility and job crafting, the influence between them, and how this relationship is built to improve performance and adaptability. For this purpose, the research has analysed which behaviours obtain the highest scores in both scales (job crafting and learning agility), designing the tool which allows Human Resources (HR) professionals an efficient identification and development behaviours to get the versatile talent that companies and professionals of the future need.

Using the questionnaire that has integrated the learning agility scale and the Spanish job crafting scale. Data were collected from a sample of business professionals in Spain. Factor analysis and hierarchical cluster analysis were used, using a classificatory variable with the 126 valid responses obtained.

In an ever-changing environment, continuous employee adaptation to his/her role within a company is a critical factor for its survival. However, there is a paucity of large-scale empirical research on which behaviours employees have to develop to increase their adaptative skills. Drawing on the outcome of extant literature, the authors identify learning agility as the construct that firms have to encourage in their employees to impact job crafting. The contribution of the paper is twofold: (1) the authors empirically explored the association and the effects of learning agility and its factor on the development of job crafting. Results demonstrated the association between the two constructs; further, higher scores in both learning agility and job crafting predict increased employability, and higher scores in job crafting are associated with higher scores in change agility; (2) this study provides a multidimensional instrument that provides HR departments with the key behaviours to recruit in order to develop talent to prepare employees to face future challenges, ensuring the right performance and sustainable impact in the environment.

A limitation of this study is that it is done exclusively within Spanish companies, even though from different industries and with different characteristics. Therefore, future research is necessary and should be conducted in other countries in similar industries to explore the empirical findings from this study in additional contexts.

This research has found a tool that might allow HR departments to measure what level of job crafting and learning agility their employees have and to identify what key behaviours they need to focus on in the recruitment or in their internal strategic HR action plan to overcome any future challenges in their organization.

In a scenario where artificial intelligence is modifying the professional landscape, generating uncertainty about which skills are best to develop, the results are a guide for enterprises as to where to focus plans for learning and training, as well as for business schools regarding the content provided in training programs.

The authors advance the literature by providing a theoretical base for understanding the relationship between job crafting and learning agility. This article offers some practical managerial recommendations that help the human resources department focus on behaviours that allow talent to be identified and recruited to ensure an effective organization.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

Doctoral students are promising entrepreneurial actors in university-based ventures, which positively impact the external environment and create value for their universities. In this article, the authors extend current research on academic entrepreneurship by shedding light on the role of university support in the early stage of Ph.D. entrepreneurship. Based on social information processing theory, the authors posit that academic entrepreneurship results from the interplay between doctoral students' human capital and university-level support. A multilevel model is proposed and empirically tested to shed light on the cradle of doctoral students' entrepreneurship by explaining the variance of their entrepreneurial alertness and intentions.

A model is proposed that explains the combined effect of specific human capital and different forms of university support on doctoral students' cognitive transition from entrepreneurial alertness to intentions. The model was then tested through structural equation modeling (SEM) and multigroup analysis (MGA) on a sample of 187 doctoral students enrolled in Italian universities.

The SEM results reveal that doctoral students' entrepreneurial alertness is influenced by perceived educational support and human capital. The MGA demonstrates that those who perceive a higher level of support for concept and business development from universities are more likely to convert their alertness into intentions than those who perceive lower support.

The present paper brings to the stage doctoral students as an extremely promising entrepreneurial target. In doing so, it extends academic entrepreneurship studies by detailing how and when the different forms of university support influence their entrepreneurial decisions, along with individual dimensions.