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This paper explores the collaborative dynamics and dimensions within a virtual multi-cultural and interdisciplinary workplace. The study focusses on the use of online communication technologies to enhance social inclusion and networking within academia.

This study uses an autoethnographic approach to draw on the personal experiences of a team of four scholars, including three early-career researchers and a senior scholar. Their reflections on their academic positionality and the institutional constraints reveal both the strengths and vulnerabilities of collaborating in a virtual workplace.

The findings offer insights into the complexities of navigating social dynamics, such as delegating responsibilities, organising meetings across various time zones and encouraging continuous collaboration, inclusivity and effective communication during an extensive timeline. As a result, their experiences revealed that a virtual workplace culture with similar and different attributes to a “normal” workplace emerged.

The paper demonstrates how to create an effective and inclusive virtual workplace by exemplifying best practices in academia and providing practical guidance for individuals and institutions based on honest, co-produced autoethnographic reflections of the authors’ lived experiences.

This study aims to investigate the intricate relationships between a community energy system, water resources and biodiversity conservation, with a specific focus on augmenting community energy resilience in Bondo. The primary objective is to gain an in-depth understanding of how community members perceive and experience the challenges related to balancing the often-conflicting demands of energy, water and biodiversity conservation within this context.

The research uses a qualitative approach to unravel the multifaceted dynamics of community energy systems, water resources and biodiversity conservation in Bondo. Data were collected through focus groups and direct observations, enabling a nuanced exploration of community perspectives and lived experiences. The subsequent analysis of this qualitative data follows established thematic analysis procedures.

The study's findings shed light on the formidable barriers that impede rural communities in Malawi from accessing electricity effectively. Even in communities fortunate enough to have electricity connections, the lack of knowledge regarding productive electricity use results in community energy systems operating at significantly reduced load factors. Furthermore, the intricate challenge of managing a biodiversity hotspot persists, exacerbated by the densely populated peripheral communities' continued reliance on forest, land and water resources. These activities, in turn, contribute to ecosystem degradation.

In a context where government-led management of forest reserves and game reserves has not yielded the expected results due to a multitude of factors, there arises a compelling need for innovative approaches. One such innovation involves fostering partnerships between the government and experienced trusts as lead organisations, providing a fresh perspective on addressing the complex interplay between community energy systems, water resources and biodiversity conservation. This novel approach opens doors to explore alternative pathways for achieving the delicate balance between human energy needs and the preservation of vital ecosystems.

The purpose of this research is to investigate the short-term capital markets' reactions to the public announcement first local detection of novel corona virus (COVID 19) cases in 12 major Asian capital markets.

Using the constant mean return model and the market model, an event study methodology has been implied to determine the cumulative abnormal returns (CARs) of 10 pre and post-event trading days. The statistical significance of the data was assessed using both parametric and nonparametric test statistics.

First discovery of local COVID 19 cases had a substantial impact on all 12 Asian markets on the event day, as shown by statistically significant negative average abnormal return (AAR) and cumulative average abnormal return (CAAR). The single factor ANOVA result has also demonstrated that there is no variability among 12 regional markets in terms of short-term market responses. Furthermore, there is little evidence that these major Asian stock market indices differ significantly from the FTSE All-World Index which might suggest possible spillover impact and co-integration among the major Asian capital markets. The study further discovers that market capitalization and liquidity did not have any significant impact on market reaction to announcement.

The study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis.

These findings will be considerably helpful in tackling this unprecedented epidemic issue for personal and institutional investors, industrial and economic experts, government and policymakers in assessing the market in special circumstances, diversifying risk and developing financial and monetary policy proposals.

This paper is the first to examine the effects of local COVID 19 detection announcement on major Asian capital markets. This study will add to the literature by investigating unusual market returns generated by infectious illness outbreaks and the overall market efficiency and investors' behavioral pattern of major Asian capital markets.

This research examines the relationship between market power and liquidity creation in the specific context of bank profitability in the Vietnamese banking sector.

The study applies the methodology proposed by Berger and Bouwman (2009) to demonstrate the creation of bank liquidity through a three-step procedure for investigating the relationship between market power and liquidity creation. The three steps include non-fat liquidity (NFLC), fat liquidity (FLC) and system generalized method of moments estimation for panel data.

This study finds that liquidity creation increases when a bank has high market power. Further, highly profitable banks positively impact the market power of banks with regard to liquidity creation, relative to less profitable banks. Moreover, bank size, capital, economic growth and interest rate negatively influence bank liquidity creation, while credit risk positively relates to bank liquidity creation.

Measurements used in this study are based on the works of Berger and Bouwman (2009). There are specific variations, relative to Basel III. In addition, other variables significantly impact bank liquidity creation that have not been considered in the models, and a quadratic model should have been considered to measure market power and bank liquidity creation.

This study suggests that managers should control the liquidity of their banks by supervising vulnerable characteristics that have been mentioned herein and emphasizing improvements in profitability. Further, the government may consider encouraging banks to generate more liquidity by modifying regulations concerned with market power or reinforcing policies about improving the transparent business environment.

This study characterizes an attempt to examine the influence of market power on the liquidity creation of banks in Vietnam, which represents one of the most dynamic systems in Asia, with several varied participating banks. The current study also examines the same within the specific context of the modifying impact of the profitability of banks.

Interorganizational collaboration has been a major source of exploratory innovation. Despite much research, the authors’ understanding about how partner cultural distance is harnessed for exploratory innovation is limited. The authors’ conceptual framework aims to address this gap by explaining the social-psychological processes between perceived partner cultural distance and exploratory innovation.

Drawing on research in organizational learning and culture mixing, the authors propose a multilevel model with two parallel processes – cultural brokering and cultural defense. If managers are engaged in the former and are protected from the latter, then the partnership will produce more exploratory innovation. Cultural brokering is encouraged by prompting a learning mindset, while cultural defense is preempted by dampening social categorization across organizational boundaries.

Cultural brokering can be encouraged by building operational-level managers' (OLMs') collaborative strength through developing a learning orientation, allowing them delivery for exploration, cultivating mutual trust with partners. Cultural defense can be preempted by protecting OLMs from intergroup anxieties through providing organizational support to the OLMs, bridging social categorization faultlines and setting shared collaborative goals. Whether an alliance can unleash its potential depends on not just how cultural brokering is enabled but also how cultural defense is curtailed.

This paper takes a microfoundational approach and considers micro-level processes in a partnership. Furthermore, the model takes the operational managers' perspective and defines culture at the organizational level. All these differences allow us to provide a nuanced picture of how diverse partnerships can be harnessed for exploratory innovation through a few easily-implementable measures.

Predicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.

In this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.

Experimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.

This study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.