Search results

This study investigates the mediating role of organizational change capacity (OCC) in the relationship between the perception of extreme contexts and firm product and process innovation, which was not empirically investigated in the literature. In addition, this study explores the moderating role of the perception of extreme contexts-related variables, which were not operationalized in ordinary firms, on the relationship between OCC and firm product and process innovation.

A questionnaire-based research was conducted to test the suggested hypotheses. The data were gathered from 90 firms during the peak period of COVID-19.

This study shows that OCC, which covers contexts, process and learning dimensions, fully mediates the relationship between the perception of extreme contexts and firm product and process innovation. Also, this study discovers that the perception of extreme contexts, including temporal ordering of extremity, the magnitude of consequences, proximity among people and operational deficiencies, positively moderate the relationship between OCC and firm product innovation.

This study has constraints inherited in survey design, primarily sampling and country context.

This study identifies, conceptualizes and operationalizes the term extreme context, conceptually argued for particular organizations/units in ordinary/mundane organization settings so far. In addition, this study extends the current understanding of how the perception of extreme contexts interacts with a firm's capability to increase innovation efforts. Further, this study shows how OCC mediates the relationship between extreme contexts and firm product and process innovation.

This research helps firms pursue an open innovation strategy but want to minimize competitive pressure from other external entities. A theoretical framework is constructed to analyze the impact of openness on innovation performance, exploring different effect of firms' external search channels.

This paper employs a stepwise hierarchical regression approach to assess the effect of openness on technological innovation considering the role of information technology adoption and political ties. The effect is conducted using a large-scale sample of 1,073 Chinese manufacturing firms over the period 2011–2013 as empirical research objects.

There are two stages of the open technological innovation process while the information technology (IT) adoption and political ties are the key consideration in emerging markets. Openness is curvilinearly (taking an inverted U-shape) related to innovation performance. Both information technology adoption and political ties generally help firms to turn broadly sourced external knowledge into technological innovation performance. This will stimulate “one plus one is greater than two” effect not only in the process of achieving performance goals, but also in the process of technological innovation.

This quantitative research illustrates the importance relationship between firms' open behaviors and technological innovation performance in emerging markets. It helps us understand firms' current constrains of open strategy of technological innovation and helps domestic or foreign investors to make strategic collaboration choices in emerging economies according to the degree of openness, informatization level, political connections, which is equally important for research and practice.

The purpose of this paper is to determine the most efficient hotels in the Indian hotel industry, the competitive positioning of these hotels, and the factors that affect their efficiency change.

This study conducts a two-stage analysis and uses data envelopment analysis (DEA) and Global Malmquist productivity index (MPI) approach in the first stage to calculate the managerial performance of a panel of 63 Indian hotels in 2019–2020 and their efficiency change from 2009–2010 to 2019–2020. Bootstrapped generalized least square (GLS) approach is applied in the second stage to evaluate the impact of contextual variables on efficiency change.

Using the results of the first stage analysis, the authors categorized the 63 Indian hotels into 7 distinct clusters. These clusters represent different levels of competitiveness and pace of growth. The GLS regression reveals a U-shaped relationship between hotel size and efficiency change and a negative relationship between pro social investments and efficiency.

This is the first study in the hotel industry that has used global MPI as a measure of efficiency change in the first stage and GLS in the second stage. In the Indian context, to the best of authors’ knowledge, no such study exists.

The case revolves around a critical incident that took place at an Indian pharmaceutical company, in which various stakeholders had very different perspectives regarding the nature, causes and consequences of the incident. By illustrating the contrasting perceptions of the same event, the authors have shed light on the nature of perception and perceptual processes, including cognitive biases and errors in human judgement. The case provides insights into how these manifest in the organisational context and how managers could be made more aware of them to avoid errors in judgment and make choices that are more informed.

The main purpose of this study is to investigate the agriculture-induced environmental Kuznets curve (EKC) hypothesis in South Asian economies (SAE).

This study employs econometric techniques, including Westerlund cointegration tests, cross-sectional augmented distributive lag model (CS-ARDL) and Dumitrescu and Hurlin (DH) causality tests to investigate the relationship between renewable and non-renewable energy consumption, agriculture, economic growth, financial development and carbon emissions in SAE from 1990 to 2019.

The CS-ARDL test outcome supports the presence of the agriculture-induced EKC hypothesis in SAE. Additionally, through the application of the DH causality test, the study confirms a unidirectional causality running from renewable energy consumption (REC), fossil fuel consumption (FFC), economic growth (GDP) and squared economic growth (GDP²) to carbon dioxide (CO₂) emissions.

This study proposes that future research should extend comparisons to worldwide intergovernmental bodies, use advanced econometric methodologies for accurate estimates, and investigate incorporating the service or primary sector into the EKC. Such multidimensional studies can inform various methods for mitigating global climate change and ensuring ecological sustainability.

Environmental degradation has been extensively studied in different regions and countries, but SAE face significant constraints in addressing this issue, and comprehensive studies in this area are scarce. This research is pioneering as it is the first study to investigate the applicability of the agriculture-induced EKC in the South Asian region. By filling this gap in the current literature, the study provides valuable insights into major SAE and their environmental challenges.

The authors target the interrelationships between non-fungible tokens (NFTs), decentralized finance (DeFi) and carbon allowances (CA) markets during 2021–2023. The recent shift of crypto and DeFi miners from China (the People's Republic of China, PRC) green hydro energy to dirty fuel energies elsewhere induces investments in carbon offsetting instruments; this is a backdrop to the authors’ investigation.

The quantile vector autoregression (VAR) approach is employed to examine extreme-quantile-connectedness and spillovers among the NFT Index (NFTI), DeFi Pulse Index (DPI), KraneShares Global Carbon Strategy ETF price (KRBN) and the Solactive Carbon Emission Allowances Rolling Futures Total Return Index (SOLCARBT).

At bull markets, DPI is the only consistent net shock transmitter as NFTI transmits innovations only at the most extreme quantile. At bear markets, KRBN and SOLCARBT are net shock transmitters, while NFTI is the only consistent net shock receiver. The receiver-transmitter roles change as a function of the market conditions. The increases in the relative tail dependence correspond to the stress events, which make systemic connectedness augment, turning market-specific idiosyncratic considerations less relevant.

The shift of digital asset miners from the PRC has resulted in excessive fuel energy consumption and aggravated environmental consequences regarding NFTs and DeFi mining. Although there exist numerous studies dedicated to CA trading and its role in carbon print reduction, the direct nexus between NFT, DeFi and CA has never been addressed in the literature. The originality of the authors’ research consists in bridging this void. Results are valuable for portfolio managers in bull and bear markets, as the authors show that connectedness is more intense under such conditions.

The present study used the job-demands and resources (JD-R) framework to understand how the training is transferred to an extreme working context through the analysis of job and personal resources (social support from the leader and colleagues and adaptability). Specifically, the authors tested the mediating role of motivation to transfer in the relationship (1) between the perceived support from the supervisor and colleagues and performance after training and (2) between adaptability and performance in an extreme context of the pandemic crisis – the first peak of COVID-19 in Portugal. Further, an inspection of the factors that predicted knowledge transfer and adaptability under an extreme context was carried out.

To do so, necessary training about the new safety rules regarding the pandemic crisis of COVID-19 was implemented in a healthcare institution as a strategy to help healthcare workers deal with the increasing uncertainty and complexity that was threatening their work. It consisted of three sessions (each with one hour of training) regarding procedures, rules and safety norms. The training occurred in May 2020. Overall, 291 healthcare workers participated in the study and answered one online questionnaire one week after training completion.

The results showed that the motivation to transfer had a significant indirect effect on the relationship between colleagues' and supervisors' support and performance and between adaptability and performance. Additionally, complementary analyses showed that the mediations depended on the levels of self-efficacy in such a way that the indirect relationships were stronger when self-efficacy was higher. Thus, adaptability and support, both from colleagues and the supervisor, are determining factors for knowledge transfer and resultant performance in extreme contexts, such as the COVID-19 pandemic crisis. Lastly, the results showed that the most significant predictors of transference were self-efficacy and the motivation to transfer the learned knowledge. On the other hand, self-efficacy, peer support and the opportunity to use the knowledge were the most significant predictors of adaptability.

These findings provide support for the role of employee motivation to transfer as a mechanism connecting both perceived support and adaptability to performance outcomes under extreme working contexts.

This study, conducted in the middle of the COVID-19 pandemic context – an extreme and uncertain working context – shows the relevance of both job and individual factors to predict employees' adaptability to such contexts.