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Identity, positioning and possibilities intersect differently for South Asian women in white academia. Within a broader migrant community that defines Australian life, these identities and positioning imply great possibility, but pursuing such pathways within academia is a walk on the last strand of resilience. This paper explores this tension of possibilities and constraints, using hope theory to highlight the cognitive resistance evident in the narratives of three South Asian women in Australian academia.

The authors use collaborative autoethnography to share their narratives of working in Australian universities at three different stages of careers, utilising Snyder's model of hope theory to interrogate their own goal-setting behaviours, pathways and agentic thinking.

The authors propose that hope as a cognitive state informs resistance and enables aspirations to contribute within academia in meaningful ways whilst navigating the terrain of inequitable structures.

The authors' use of hope theory as a lens on the intersectional experiences of career making, building and progression is a new contribution to scholarship on marginalised women in white academe and the ways in which the pathways of resistance are identified.

Environmental knowledge management (EKM) has been studied mainly owing to the increasing awareness of environmental issues. Such issues have generated a warning in the tourism industry that has stimulated a new wave of research on EKM. EKM forges landscape characteristics and so destination image. In turn, EKM sounds affecting tourism destination which calls for destination personality which shows a research context less explored. From a knowledge management perspective, The present research aims to investigate on EKM to understand how it leverages tourists' and destination personality.

With the intent of exploring EKM, the research uses a quantitative analysis on a sample of 2,222 young Chinese tourists. In this context, EKM is linked with destination’s personality and tourists’ personalities, their satisfaction with the destination and their behavioral intentions.

By SPSS regression model, EKM and destination personality are positively linked. This positive relationship is also reflected on destination personality and destination satisfaction, behavioral intention.

The authors’ original contribution to the knowledge management literature extends the new wave of research on EKM. The research also proves the need to make a close collaboration between tourists, the local community and marketers. Marketers need to pay more attention to what tourists want to do and see in the place visited. In a nutshell, there is the need of enforcing and promoting EKM.

This study presents a model for assessing the effects of employee ICT training on organizations’ results. It also introduces digital transformation as a mediator between the two concepts and studies the role of organizational commitment and human capital in terms of digital transformation.

Surveys were completed by the CEOs of 184 Spanish companies, and their responses were analyzed with Partial Least Squares.

The results empirically analyze the proposed theoretical model and highlight the fact that human capital and organizational commitment partially mediate the link between ICT training and digital transformation. Furthermore, there is a direct relationship between ICT training and company performance.

Directors and managers should invest more resources in the human capital of their company through ICT training. In fact, it can improve organizational commitment, encouraging employees to adopt innovative behaviors, thus allowing for the necessary digital transformation.

Despite heavy theoretical emphasis on the study of the conditions necessary for the digital transformation of companies, few studies have empirically analyzed the effects of adopting certain practices for its implementation. This paper focuses on analyzing the effect of ICT training, which is configured as a tool capable of improving staff knowledge and increasing employee commitment. This is essential for adopting organizational change such as digital transformation.

The purpose of this study is to explore and present a clear overview of innovation topics during the first year of the COVID-19 pandemic, and then organise these topics into various analyses.

The authors use multiple language analysis methods, such as text mining and latent Dirichlet allocation topic modelling, to address the research questions. A total of 440 news articles are analysed using Python and Google Colaboratory tools.

The analysis identified 20 innovation topics, highlighted sector-specific analyses and proposed phases of innovation. The authors suggest that each sector develops unique patterns and forms of innovation for long-term benefits and further research. This study expands upon existing literature on innovation and crisis at a theoretical level by incorporating an actor as the agency.

Based on the findings, the authors conclude that the COVID-19 pandemic has prompted businesses to adopt dynamic capabilities. Furthermore, the authors provide several strategic recommendations for addressing the pandemic in the developing context. The study discusses the roles of policymakers, business practitioners and academia in this context as well.

Very few studies specifically explore and identify forced innovation topics in emerging countries during the pandemic. There has been no review of forced innovations implemented in Indonesia using news media as a source. Additionally, this study presents the trajectory of innovation during the time of crises.

Social media are emerging as the ideal channel for building one-to-many communication and disseminating intellectual capital (IC) information. Their rise is bringing out new research challenges to investigate the implications of their use. However, there needs to be more research contributions relating to the financial benefits of using social media for IC disclosure (ICD). This study aims to bridge this gap by analyzing, under the lens of signaling theory, the effect of ICD through Twitter on firm value.

This study is based on a content analysis of tweets disseminated by 262 companies aimed at examining the amount of IC information disclosed and on a regression analysis aimed at analyzing the impact of this type of information on firm value.

Empirical results show that a large ICD via Twitter favors an increase in firm value. They also demonstrate that disclosing information relating to the three IC dimensions positively affects the firm value. These findings suggest that actively and comprehensively communicating IC information via Twitter can help improve the perception and evaluation of the company by investors and other stakeholders.

This study offers empirical evidence about the financial benefits associated with using social media as disclosure tools by companies. It also enriches the literature on the relationship between ICD and firm value and consolidates the goodness of the signaling theory as an ideal theoretical perspective to frame the relationship between IC information and firm value.

This study offers important managerial implications for firms and investors. In light of the significant financial benefits, firms should use social media to disclose IC information and should seek to increase their visibility on such platforms to convey the information to a greater number of users. Investors should also heed social media when gathering IC information, combining the analysis of these platforms with that of traditional corporate documents.

This study enriches the limited literature on ICD via social media and extends knowledge about the relationship between IC information and firm value. In this regard, the originality also lies in the individual analysis of the impact of the three IC dimensions on firm value.

The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country.

A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift.

Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind.

This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.

While there was heightened awareness on the technologies of the fourth industrial revolution (IR 4.0) prior to COVID-19, studies have shown that the adoption of these advanced technologies (e.g. Big Data, robotics, Internet of Things, etc.) continues to remain low across global industries. This qualitative study sought to explore the reasons for the low rate of adoption of these technologies and appropriate measures to enhance their adoption at hotels, through the lens of hotel executives.

Based on interpretivist's ideals, this study follows a case study design and adopts a qualitative method of enquiry. The heterogenous purposive sampling technique was employed to gather data for the study, using semi-structured interviews.

Grounded on the technology-organisation-environment (TOE) framework, the thematic analysis revealed technology, organisation and environment-related reasons for the low rate of IR 4.0 technologies adoption at hotels in Malaysia. Also, the study uncovered some interesting measures that will enhance the adoption of these advanced technologies at hotels.

This study unearths technology, organisation and environment-related reasons for low adoption, and measures to enhance the adoption of IR 4.0 technologies in hotels. This study also enlightens hotel owners and technology providers about practical issues that will ensure the successful adoption of such technologies to enhance hotel business. In line with SDG 9, this study also seeks to promote sustainable innovation in the hospitality industry. Practical and theoretical implications have also been discussed in this study.

The purpose of this paper is to identify the causal (not correlational) effect of women’s entrepreneurship on corporate social responsibility (CSR) practices in Latin America.

This study builds on a hitherto unexploited sparse data set on Latin American B Corporations to identify the causal relationship of interest and on a (synthetic) instrumental variable method.

The results confirm that women’s entrepreneurship has a positive causal effect on social responsibility. This study finds that an increase of 1% in the proportion of women entrepreneurs leads to an increase of 0.5 in the B Impact Assessment score, the CSR indicator.

This study contributes to the literature by providing robust statistical evidence of a causal relationship between women entrepreneurs and social responsibility practices in the Latin American context. This research captures the multidimensional nature of social responsibility by using a comprehensive and vast metric of CSR obtained from the data of the B Impact Assessment tool. This study illustrates how machine learning methods can be used to address the lack of structure of the Latin American B Impact Assessment data.

El propósito de esta investigación es identificar el efecto causal (no correlacional) del emprendimiento de mujeres en las prácticas de responsabilidad social empresarial (RSE) en América Latina.

Nos basamos en un conjunto de datos escasamente explorado hasta el momento sobre las Empresas B en América Latina para identificar la relación causal de interés, y utilizamos un método de Variables Instrumentales (VI) sintéticas.

Nuestros resultados verifican el efecto causal positivo del emprendimiento de las mujeres en la responsabilidad social. Descubrimos que un aumento del 1% en la proporción de mujeres emprendedoras conduce a un aumento de 0.5 en la puntuación de la Evaluación de Impacto B, nuestro indicador de RSE.

Contribuimos a la literatura proporcionando evidencia estadística sólida de una relación causal entre emprendedoras mujeres y prácticas de responsabilidad social en el contexto de América Latina. Esta investigación captura la naturaleza multidimensional de la responsabilidad social mediante el uso de una métrica amplia y vasta de RSE obtenida de los datos de la herramienta de Evaluación de Impacto B. Ilustramos cómo se pueden utilizar métodos de aprendizaje automático para abordar la falta de estructura de los datos de evaluación de impacto B en América Latina.

O propósito desta pesquisa é identificar o efeito causal (não correlacional) do empreendedorismo feminino nas práticas de responsabilidade social corporativa (RSC) na América Latina.

Baseamo-nos em um conjunto de dados escasso até então não explorado sobre as Empresas B na América Latina para identificar a relação causal de interesse, e utilizamos um método de Variáveis Instrumentais (VI) sintéticas.

Nossos resultados verificam o efeito causal positivo do empreendedorismo feminino na responsabilidade social. Descobrimos que um aumento de 1% na proporção de mulheres empreendedoras leva a um aumento de 0,5 no escore de Avaliação de Impacto B, nosso indicador de RSC.

Contribuímos para a literatura fornecendo evidências estatísticas robustas de uma relação causal entre empreendedoras mulheres e práticas de responsabilidade social na América Latina. Esta pesquisa captura a natureza multidimensional da responsabilidade social usando uma métrica abrangente e vasta de RSC obtida a partir dos dados da ferramenta de Avaliação de Impacto B. Ilustramos como métodos de aprendizado de máquina podem ser usados para lidar com a falta de estrutura dos dados de avaliação de impacto B na América Latina.

Unemployment is one of the major challenges facing most countries, including Africa as a continent. Seeking how to reduce unemployment, debt, inflation and increase gross domestic product (GDP), foreign direct investment (FDI) and gross capital formation in the continent has been an agenda of governments, policy makers and economists to. This study examines the relationship between economic growth, inflation, debt, FDI, gross capital formation, labor force, population and unemployment in Africa.

An updated panel dataset of 29 African countries was selected from different regions from 1991 to 2019. These countries were selected based on their unemployment, population growth and inflation rates. The Pesaran cross-sectional dependence and panel unit root test (the Dickey–Fuller cross-sectional supplemented and the Im-Pesaran-Shin cross-sectional) were applied. Further, the panel Autoregressive Distributed Lag (ARDL) model (Bounds test) and pooled mean group (PMG) estimator were utilized in this work.

This shows that economic growth, debt, labor force and population have a positive relationship with unemployment in the long run. Therefore, an increase in these variables generates an increase in the selected African countries' unemployment growth. In contrast, inflation, FDI and gross capital formation have a negative relationship with unemployment in the long run, which implies that an increase in these variables reduces unemployment in the selected African countries.

This study has potential limitations because some data from the countries are not up to date and some years are missing from the data.

This study contributes to understanding unemployment and Okun's law in the African economy. This study shows that an increase in economic growth leads to a rise in unemployment, while an increase in inflation leads to a decrease in unemployment.

This paper provides an insight into the major factors that increase and reduces unemployment for government and policy marker to take the adequate measure.

There has been an unprecedented increase in online learning worldwide, including in teacher education. However, student lurking can be a common issue, leading to a non-interactive learning environment.

The authors employed a qualitative case study with thematic analysis to examine a novel “self-directed” pre-service teacher online degree module that engaged students in regular peer-feedback, which intended to promote student engagement and interactivity. The research questions were as follows: To what extent did the seminar series represent the principles of self-directed learning and were learning outcomes effective from the process? And, how effective was the use of peer feedback?

The thematic analysis revealed that student progression and course completion was successful, and it represented some principles of self-directed learning; but (a) it cannot be presumed that pre-service teachers are competent in giving (peer) feedback and (b) pre-service teachers may need specific guidance and training for providing competent feedback.

This paper is highly original in respect of its combination of the self-directed learning framework with use of peer feedback, to engage students in an interactive learning environment. The present paper identifies that peer feedback is a powerful tool in online learning; peer feedback can supplement self- and teacher-assessment; but it should not be assumed that pre-service teachers are competent in providing (peer) feedback – pre-service teachers may need specific training in providing feedback.